def main():
import optparse
usage = "usage: %prog [options] inputAudioFile"
parser = optparse.OptionParser(usage)
# Name of the output files:
parser.add_option("-v", "--vocal-output-file",
dest="voc_output_file", type="string",
help="name of the audio output file for the estimated\n"\
"solo (vocal) part",
default="estimated_solo.wav")
parser.add_option("-m", "--music-output-file",
dest="mus_output_file", type="string",
help="name of the audio output file for the estimated\n"\
"music part",
default="estimated_music.wav")
parser.add_option("-p", "--pitch-output-file",
dest="pitch_output_file", type="string",
help="name of the output file for the estimated pitches",
default="pitches.txt")
# Some more optional options:
parser.add_option("-d", "--with-display", dest="displayEvolution",
action="store_true",help="display the figures",
default=False)
parser.add_option("-q", "--quiet", dest="verbose",
action="store_false",
help="use to quiet all output verbose",
default=True)
parser.add_option("--nb-iterations", dest="nbiter",
help="number of iterations", type="int",
default=100)
parser.add_option("--window-size", dest="windowSize", type="float",
default=0.04644,help="size of analysis windows, in s.")
parser.add_option("--Fourier-size", dest="fourierSize", type="int",
default=2048,
help="size of Fourier transforms, "\
"in samples.")
parser.add_option("--hopsize", dest="hopsize", type="float",
default=0.0058,
help="size of the hop between analysis windows, in s.")
parser.add_option("--nb-accElements", dest="R", type="float",
default=40.0,
help="number of elements for the accompaniment.")
parser.add_option("--with-melody", dest="melody", type="string",
default=None,
help="provide the melody in a file named MELODY, "\
"with at each line: <time (s)><F0 (Hz)>.")
(options, args) = parser.parse_args()
if len(args) != 1:
parser.error("incorrect number of arguments, use option -h for help.")
displayEvolution = options.displayEvolution
if displayEvolution:
import matplotlib.pyplot as plt
import imageMatlab
## plt.rc('text', usetex=True)
plt.rc('image',cmap='jet') ## gray_r
plt.ion()
# Compulsory option: name of the input file:
inputAudioFile = args[0]
fs, data = wav.read(inputAudioFile)
# data = np.double(data) / 32768.0 # makes data vary from -1 to 1
scaleData = 1.2 * data.max() # to rescale the data.
dataType = data.dtype
data = np.double(data) / scaleData # makes data vary from -1 to 1
tmp = np.zeros((data.size, 2))
tmp[:,0] = data
tmp[:,1] = data
data = tmp
if data.shape[0] == data.size: # data is multi-channel
print "The audio file is not stereo. Try separateLead.py instead."
raise ValueError("number of dimensions of the input not 2")
if data.shape[1] != 2:
print "The data is multichannel, but not stereo... \n"
print "Unfortunately this program does not scale well. Data is \n"
print "reduced to its 2 first channels.\n"
data = data[:,0:2]
# Processing the options:
windowSizeInSamples = np.round(options.windowSize * fs)
hopsize = np.round(options.hopsize * fs)
NFT = options.fourierSize
niter = options.nbiter
R = options.R
if options.verbose:
print "Some parameter settings:"
print " Size of analysis windows: ", windowSizeInSamples
print " Hopsize: ", hopsize
print " Size of Fourier transforms: ", NFT
print " Number of iterations to be done: ", niter
print " Number of elements in WM: ", R
XR, F, N = stft(data[:,0], fs=fs, hopsize=hopsize,
window=sinebell(windowSizeInSamples), nfft=NFT)
XL, F, N = stft(data[:,1], fs=fs, hopsize=hopsize,
window=sinebell(windowSizeInSamples), nfft=NFT)
# SX is the power spectrogram:
## SXR = np.maximum(np.abs(XR) ** 2, 10 ** -8)
## SXL = np.maximum(np.abs(XL) ** 2, 10 ** -8)
SXR = np.abs(XR) ** 2
SXL = np.abs(XL) ** 2
del data, F, N
# TODO: also process these as options:
eps = 10 ** -9
minF0 = 100
maxF0 = 800
Fs = fs
F, N = SXR.shape
stepNotes = 20 # this is the number of F0s within one semitone
# until 17/09/2010 : stepNotes = 20
# 17/09/2010 : trying stepNotes = 8, checking for less artefacts
K = 10 # number of spectral shapes for the filter part
# R = 40 # number of spectral shapes for the accompaniment
P = 30 # number of elements in dictionary of smooth filters
chirpPerF0 = 1 # number of chirped spectral shapes between each F0
# this feature should be further studied before
# we find a good way of doing that.
# Create the harmonic combs, for each F0 between minF0 and maxF0:
F0Table, WF0 = \
generate_WF0_chirped(minF0, maxF0, Fs, Nfft=NFT, \
stepNotes=stepNotes, \
lengthWindow=windowSizeInSamples, Ot=0.25, \
perF0=chirpPerF0, \
depthChirpInSemiTone=.15, loadWF0=True,\
analysisWindow='sinebell')
WF0 = WF0[0:F, :] # ensure same size as SX
NF0 = F0Table.size # number of harmonic combs
# Normalization:
WF0 = WF0 / np.outer(np.ones(F), np.amax(WF0, axis=0))
# Create the dictionary of smooth filters, for the filter part of
# the lead isntrument:
WGAMMA = generateHannBasis(F, NFT, Fs=fs, frequencyScale='linear', \
numberOfBasis=P, overlap=.75)
if displayEvolution:
plt.figure(1);plt.clf()
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel(r'Frame number $n$', fontsize=16)
plt.ylabel(r'Leading source number $u$', fontsize=16)
plt.ion()
# plt.show()
## the following seems superfluous if mpl's backend is macosx...
## raw_input("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"\
## "!! Press Return to resume the program. !!\n"\
## "!! Be sure that the figure has been !!\n"\
## "!! already displayed, so that the !!\n"\
## "!! evolution of HF0 will be visible. !!\n"\
## "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
if options.melody is None:
## section to estimate the melody, on monophonic algo:
SX = np.maximum(np.abs((XR + XL) / 2.0) ** 2, 10 ** -8)
# First round of parameter estimation:
HGAMMA, HPHI, HF0, HM, WM, recoError1 = SIMM.SIMM(
# the data to be fitted to:
SX,
# the basis matrices for the spectral combs
WF0,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K, numberOfAccompanimentSpectralShapes=R,
# putting only 2 elements in accompaniment for a start...
# if any, initial amplitude matrices for
HGAMMA0=None, HPHI0=None,
HF00=None,
WM0=None, HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter, updateRulePower=1.,
stepNotes=stepNotes,
lambdaHF0 = 0.0 / (1.0 * SX.max()), alphaHF0=0.9,
verbose=options.verbose, displayEvolution=displayEvolution)
if displayEvolution:
h2 = plt.figure(2);plt.clf();
imageMatlab.imageM(20 * np.log10(HF0))
matMax = (20 * np.log10(HF0)).max()
matMed = np.median(20 * np.log10(HF0))
plt.clim([matMed - 100, matMax])
# Viterbi decoding to estimate the predominant fundamental
# frequency line
scale = 1.0
transitions = np.exp(-np.floor(np.arange(0,NF0) / stepNotes) * scale)
cutoffnote = 2 * 5 * stepNotes
transitions[cutoffnote:] = transitions[cutoffnote - 1]
transitionMatrixF0 = np.zeros([NF0 + 1, NF0 + 1]) # toeplitz matrix
b = np.arange(NF0)
transitionMatrixF0[0:NF0, 0:NF0] = \
transitions[\
np.array(np.abs(np.outer(np.ones(NF0), b) \
- np.outer(b, np.ones(NF0))), dtype=int)]
pf_0 = transitions[cutoffnote - 1] * 10 ** (-90)
p0_0 = transitions[cutoffnote - 1] * 10 ** (-100)
p0_f = transitions[cutoffnote - 1] * 10 ** (-80)
transitionMatrixF0[0:NF0, NF0] = pf_0
transitionMatrixF0[NF0, 0:NF0] = p0_f
transitionMatrixF0[NF0, NF0] = p0_0
sumTransitionMatrixF0 = np.sum(transitionMatrixF0, axis=1)
transitionMatrixF0 = transitionMatrixF0 \
/ np.outer(sumTransitionMatrixF0, \
np.ones(NF0 + 1))
priorProbabilities = 1 / (NF0 + 1.0) * np.ones([NF0 + 1])
logHF0 = np.zeros([NF0 + 1, N])
normHF0 = np.amax(HF0, axis=0)
barHF0 = np.array(HF0)
logHF0[0:NF0, :] = np.log(barHF0)
logHF0[0:NF0, normHF0==0] = np.amin(logHF0[logHF0>-np.Inf])
logHF0[NF0, :] = np.maximum(np.amin(logHF0[logHF0>-np.Inf]),-100)
indexBestPath = viterbiTrackingArray(\
logHF0, np.log(priorProbabilities),
np.log(transitionMatrixF0), verbose=options.verbose)
if displayEvolution:
h2.hold(True)
plt.plot(indexBestPath, '-b')
h2.hold(False)
plt.axis('tight')
## raw_input("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"\
## "!! Press Return to resume the program !!\n"\
## "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
del logHF0
# detection of silences:
HF00 = np.zeros([NF0 * chirpPerF0, N])
scopeAllowedHF0 = 2.0 / 1.0
dim1index = np.array(\
np.maximum(\
np.minimum(\
np.outer(chirpPerF0 * indexBestPath,
np.ones(chirpPerF0 \
* (2 \
* np.floor(stepNotes / scopeAllowedHF0) \
+ 1))) \
+ np.outer(np.ones(N),
np.arange(-chirpPerF0 \
* np.floor(stepNotes / scopeAllowedHF0),
chirpPerF0 \
* (np.floor(stepNotes / scopeAllowedHF0) \
+ 1))),
chirpPerF0 * NF0 - 1),
0),
dtype=int).reshape(1, N * chirpPerF0 \
* (2 * np.floor(stepNotes / scopeAllowedHF0) \
+ 1))
dim2index = np.outer(np.arange(N),
np.ones(chirpPerF0 \
* (2 * np.floor(stepNotes \
/ scopeAllowedHF0) + 1), \
dtype=int)\
).reshape(1, N * chirpPerF0 \
* (2 * np.floor(stepNotes \
/ scopeAllowedHF0) \
+ 1))
HF00[dim1index, dim2index] = HF0[dim1index, dim2index]# HF0.max()
HF00[:, indexBestPath == (NF0 - 1)] = 0.0
HF00[:, indexBestPath == 0] = 0.0
thres_energy = 0.000584
SF0 = np.maximum(np.dot(WF0, HF00), eps)
SPHI = np.maximum(np.dot(WGAMMA, np.dot(HGAMMA, HPHI)), eps)
SM = np.maximum(np.dot(WM, HM), eps)
hatSX = np.maximum(SPHI * SF0 + SM, eps)
energyMel = np.sum(np.abs((SPHI * SF0)/hatSX * \
(XR+XL) * 0.5) \
** 2, axis=0)
energyMelSorted = np.sort(energyMel)
energyMelCumul = np.cumsum(energyMelSorted)
energyMelCumulNorm = energyMelCumul / max(energyMelCumul[-1], eps)
# normalized to the maximum of energy:
# expressed in 0.01 times the percentage
ind_999 = np.nonzero(energyMelCumulNorm>thres_energy)[0][0]
if ind_999 is None:
ind_999 = N
melNotPresent = (energyMel <= energyMelCumulNorm[ind_999])
indexBestPath[melNotPresent] = 0
else:
## take the provided melody line:
# load melody from file:
melodyFromFile = np.loadtxt(options.melody)
sizeProvidedMel = melodyFromFile.shape
if len(sizeProvidedMel) == 1:
print "The melody should be provided as <Time (s)><F0 (Hz)>."
raise ValueError("Bad melody format")
melTimeStamps = melodyFromFile[:,0] # + 1024 / np.double(Fs)
melFreqHz = melodyFromFile[:,1]
if minF0 > melFreqHz[melFreqHz>40.0].min() or maxF0 < melFreqHz.max():
minF0 = melFreqHz[melFreqHz>40.0].min() *.97
maxF0 = np.maximum(melFreqHz.max()*1.03, 2*minF0 * 1.03)
print "Recomputing the source basis for "
print "minF0 = ", minF0, "Hz and maxF0 = ", maxF0, "Hz."
# Create the harmonic combs, for each F0 between minF0 and maxF0:
F0Table, WF0 = \
generate_WF0_chirped(minF0, maxF0, Fs, Nfft=NFT, \
stepNotes=stepNotes, \
lengthWindow=windowSizeInSamples,
Ot=0.25, \
perF0=chirpPerF0, \
depthChirpInSemiTone=.15)
WF0 = WF0[0:F, :] # ensure same size as SX
NF0 = F0Table.size # number of harmonic combs
# Normalization:
WF0 = WF0 / np.outer(np.ones(F), np.amax(WF0, axis=0))
sigTimeStamps = np.arange(N) * hopsize / np.double(Fs)
distMatTimeStamps = np.abs(np.outer(np.ones(sizeProvidedMel[0]),
sigTimeStamps) -
np.outer(melTimeStamps, np.ones(N)))
minDistTimeStamps = distMatTimeStamps.argmin(axis=0)
f0BestPath = melFreqHz[minDistTimeStamps]
distMatF0 = np.abs(np.outer(np.ones(NF0), f0BestPath) -
np.outer(F0Table, np.ones(N)))
indexBestPath = distMatF0.argmin(axis=0)
# setting silences to 0, with tolerance = 1/2 window length
indexBestPath[distMatTimeStamps[minDistTimeStamps,range(N)] >= \
0.5 * options.windowSize] = 0
indexBestPath[f0BestPath<=0] = 0
freqMelody = F0Table[np.array(indexBestPath,dtype=int)]
freqMelody[indexBestPath==0] = - freqMelody[indexBestPath==0]
np.savetxt(options.pitch_output_file,
np.array([np.arange(N) * hopsize / np.double(Fs),
freqMelody]).T)
# Second round of parameter estimation, with specific
# initial HF00:
HF00 = np.zeros([NF0 * chirpPerF0, N])
scopeAllowedHF0 = 2.0 / 1.0
# indexes for HF00:
# TODO: reprogram this with a 'where'?...
dim1index = np.array(\
np.maximum(\
np.minimum(\
np.outer(chirpPerF0 * indexBestPath,
np.ones(chirpPerF0 \
* (2 \
* np.floor(stepNotes / scopeAllowedHF0) \
+ 1))) \
+ np.outer(np.ones(N),
np.arange(-chirpPerF0 \
* np.floor(stepNotes / scopeAllowedHF0),
chirpPerF0 \
* (np.floor(stepNotes / scopeAllowedHF0) \
+ 1))),
chirpPerF0 * NF0 - 1),
0),
dtype=int)
dim1index = dim1index[indexBestPath!=0,:]
## dim1index = dim1index.reshape(1, N * chirpPerF0 \
## * (2 * np.floor(stepNotes / scopeAllowedHF0) \
## + 1))
dim1index = dim1index.reshape(1,dim1index.size)
dim2index = np.outer(np.arange(N),
np.ones(chirpPerF0 \
* (2 * np.floor(stepNotes \
/ scopeAllowedHF0) + 1), \
dtype=int)\
)
dim2index = dim2index[indexBestPath!=0,:]
dim2index = dim2index.reshape(1,dim2index.size)
## dim2index.reshape(1, N * chirpPerF0 \
## * (2 * np.floor(stepNotes \
## / scopeAllowedHF0) \
## + 1))
HF00[dim1index, dim2index] = 1 # HF0.max()
HF00[:, indexBestPath == (NF0 - 1)] = 0.0
HF00[:, indexBestPath == 0] = 0.0
WF0effective = WF0
HF00effective = HF00
if options.melody is None:
del HF0, HGAMMA, HPHI, HM, WM, HF00, SX
alphaR, alphaL, HGAMMA, HPHI, HF0, \
betaR, betaL, HM, WM, recoError2 = SIMM.Stereo_SIMM(
# the data to be fitted to:
SXR, SXL,
# the basis matrices for the spectral combs
WF0effective,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K, numberOfAccompanimentSpectralShapes=R,
# if any, initial amplitude matrices for
HGAMMA0=None, HPHI0=None,
HF00=HF00effective,
WM0=None, HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter, updateRulePower=1.0,
stepNotes=stepNotes,
lambdaHF0 = 0.0 / (1.0 * SXR.max()), alphaHF0=0.9,
verbose=options.verbose, displayEvolution=displayEvolution)
WPHI = np.dot(WGAMMA, HGAMMA)
SPHI = np.dot(WPHI, HPHI)
SF0 = np.dot(WF0effective, HF0)
hatSXR = (alphaR**2) * SF0 * SPHI + np.dot(np.dot(WM, betaR**2),HM)
hatSXL = (alphaL**2) * SF0 * SPHI + np.dot(np.dot(WM, betaL**2),HM)
hatVR = (alphaR**2) * SPHI * SF0 / hatSXR * XR
vestR = istft(hatVR, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
hatVR = (alphaL**2) * SPHI * SF0 / hatSXL * XL
vestL = istft(hatVR, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
#scikits.audiolab.wavwrite(np.array([vestR,vestL]).T, \
# options.voc_output_file, fs)
vestR = np.array(np.round(vestR*scaleData), dtype=dataType)
vestL = np.array(np.round(vestL*scaleData), dtype=dataType)
wav.write(options.voc_output_file, fs, \
np.array([vestR,vestL]).T)
#wav.write(options.voc_output_file, fs, \
# np.int16(32768.0 * np.array([vestR,vestL]).T))
hatMR = (np.dot(np.dot(WM,betaR ** 2),HM)) / hatSXR * XR
mestR = istft(hatMR, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
hatMR = (np.dot(np.dot(WM,betaL ** 2),HM)) / hatSXL * XL
mestL = istft(hatMR, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
#scikits.audiolab.wavwrite(np.array([mestR,mestL]).T, \
# options.mus_output_file, fs)
mestR = np.array(np.round(mestR*scaleData), dtype=dataType)
mestL = np.array(np.round(mestL*scaleData), dtype=dataType)
wav.write(options.mus_output_file, fs, \
np.array([mestR,mestL]).T)
#wav.write(options.mus_output_file, fs, \
# np.int16(32768.0 * np.array([mestR,mestL]).T))
del hatMR, mestL, vestL, vestR, mestR, hatVR, hatSXR, hatSXL, SPHI, SF0
# adding the unvoiced part in the source basis:
WUF0 = np.hstack([WF0, np.ones([WF0.shape[0], 1])])
HUF0 = np.vstack([HF0, np.ones([1, HF0.shape[1]])])
## HUF0[-1,:] = HF0.sum(axis=0) # should we do this?
alphaR, alphaL, HGAMMA, HPHI, HF0, \
betaR, betaL, HM, WM, recoError3 = SIMM.Stereo_SIMM(
# the data to be fitted to:
SXR, SXL,
# the basis matrices for the spectral combs
WUF0,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K, numberOfAccompanimentSpectralShapes=R,
# if any, initial amplitude matrices for
HGAMMA0=HGAMMA, HPHI0=HPHI,
HF00=HUF0,
WM0=None,#WM,
HM0=None,#HM,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter, updateRulePower=1.0,
stepNotes=stepNotes,
lambdaHF0 = 0.0 / (1.0 * SXR.max()), alphaHF0=0.9,
verbose=options.verbose, displayEvolution=displayEvolution,
updateHGAMMA=False)
WPHI = np.dot(WGAMMA, HGAMMA)
SPHI = np.dot(WPHI, HPHI)
SF0 = np.dot(WUF0, HF0)
hatSXR = (alphaR**2) * SF0 * SPHI + np.dot(np.dot(WM, betaR**2),HM)
hatSXL = (alphaL**2) * SF0 * SPHI + np.dot(np.dot(WM, betaL**2),HM)
hatVR = (alphaR**2) * SPHI * SF0 / hatSXR * XR
vestR = istft(hatVR, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
hatVR = (alphaL**2) * SPHI * SF0 / hatSXL * XL
vestL = istft(hatVR, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
outputFileName = options.voc_output_file[:-4] + '_VUIMM.wav'
# scikits.audiolab.wavwrite(np.array([vestR,vestL]).T, outputFileName, fs)
vestR = np.array(np.round(vestR*scaleData), dtype=dataType)
vestL = np.array(np.round(vestL*scaleData), dtype=dataType)
wav.write(outputFileName, fs, \
np.array([vestR,vestL]).T)
hatMR = (np.dot(np.dot(WM,betaR ** 2),HM)) / hatSXR * XR
mestR = istft(hatMR, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
hatMR = (np.dot(np.dot(WM,betaL ** 2),HM)) / hatSXL * XL
mestL = istft(hatMR, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
outputFileName = options.mus_output_file[:-4] + '_VUIMM.wav'
#scikits.audiolab.wavwrite(np.array([mestR,mestL]).T, outputFileName, fs)
mestR = np.array(np.round(mestR*scaleData), dtype=dataType)
mestL = np.array(np.round(mestL*scaleData), dtype=dataType)
wav.write(outputFileName, fs, \
np.array([mestR,mestL]).T)
if displayEvolution:
plt.close('all')
## raw_input("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"\
## "!! Press Return to end the program... !!\n"\
## "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
print "Done!"
def stereo_NMF(SXR, SXL,
numberOfAccompanimentSpectralShapes,
WM0=None, HM0=None,
numberOfIterations=50, updateRulePower=1.0,
verbose=False, displayEvolution=False):
eps = 10 ** (-20)
if displayEvolution:
import matplotlib.pyplot as plt
from imageMatlab import imageM
plt.ion()
print("Is the display interactive? ", plt.isinteractive())
R = numberOfAccompanimentSpectralShapes
omega = updateRulePower
F, N = SXR.shape
if (F, N) != SXL.shape:
print("The input STFT matrices do not have the same dimension.\n")
print("Please check what happened...")
raise ValueError("Dimension of STFT matrices must be the same.")
if HM0 is None:
HM0 = np.abs(randn(R, N))
else:
if np.array(HM0).shape[0] == R and np.array(HM0).shape[1] == N:
HM0 = np.array(HM0)
else:
print("Wrong dimensions for given HM0, \n")
print("random initialization used instead")
HM0 = np.abs(randn(R, N))
HM = np.copy(HM0)
if WM0 is None:
WM0 = np.abs(randn(F, R))
else:
if np.array(WM0).shape[0] == F and np.array(WM0).shape[1] == R:
WM0 = np.array(WM0)
else:
print("Wrong dimensions for given WM0, \n")
print("random initialization used instead")
WM0 = np.abs(randn(F, R))
WM = np.copy(WM0)
betaR = np.diag(np.random.rand(R))
betaL = np.eye(R) - betaR
hatSXR = np.maximum(np.dot(np.dot(WM, betaR**2), HM), eps)
hatSXL = np.maximum(np.dot(np.dot(WM, betaL**2), HM), eps)
# temporary matrices
tempNumFbyN = np.zeros([F, N])
tempDenFbyN = np.zeros([F, N])
recoError = np.zeros([numberOfIterations * 3 + 1])
recoError[0] = ISDistortion(SXR, hatSXR) + ISDistortion(SXL, hatSXL)
if verbose:
print("Reconstruction error at beginning: ", recoError[0])
counterError = 1
if displayEvolution:
h1 = plt.figure(1)
for n in np.arange(numberOfIterations):
# order of re-estimation: HF0, HPHI, HM, HGAMMA, WM
if verbose:
print("iteration ", n, " over ", numberOfIterations)
if displayEvolution:
h1.clf()
imageM(db(hatSXR))
plt.clim([np.amax(db(hatSXR))-100, np.amax(db(hatSXR))])
plt.draw()
# updating HM
HM = HM * \
((np.dot(np.dot((betaR**2), WM.T), SXR /
np.maximum(hatSXR ** 2, eps)) +
np.dot(np.dot((betaL**2), WM.T), SXL /
np.maximum(hatSXL ** 2, eps))
) /
np.maximum(np.dot(np.dot((betaR**2), WM.T), 1 /
np.maximum(hatSXR, eps)) +
np.dot(np.dot((betaL**2), WM.T), 1 /
np.maximum(hatSXL, eps)),
eps)) ** omega
hatSXR = np.maximum(np.dot(np.dot(WM, betaR**2),HM), eps)
hatSXL = np.maximum(np.dot(np.dot(WM, betaL**2),HM), eps)
recoError[counterError] = ISDistortion(SXR, hatSXR) \
+ ISDistortion(SXL, hatSXL)
if verbose:
print("Reconstruction error difference after HM : ",\
recoError[counterError] - recoError[counterError - 1])
counterError += 1
# updating WM
WM = WM * \
((np.dot(SXR / np.maximum(hatSXR ** 2, eps),
np.dot(HM.T, betaR ** 2)) +
np.dot(SXL / np.maximum(hatSXL ** 2, eps),
np.dot(HM.T, betaL ** 2))
) /
(np.dot(1 / np.maximum(hatSXR, eps),
np.dot(HM.T, betaR ** 2)) +
np.dot(1 / np.maximum(hatSXL, eps),
np.dot(HM.T, betaL ** 2))
)) ** omega
sumWM = np.sum(WM, axis=0)
WM[:, sumWM>0] = (WM[:, sumWM>0] /
np.outer(np.ones(F),sumWM[sumWM>0]))
HM = HM * np.outer(sumWM, np.ones(N))
hatSXR = np.maximum(np.dot(np.dot(WM, betaR**2), HM), eps)
hatSXL = np.maximum(np.dot(np.dot(WM, betaL**2), HM), eps)
recoError[counterError] = ISDistortion(SXR, hatSXR) \
+ ISDistortion(SXL, hatSXL)
if verbose:
print("Reconstruction error difference after WM : ",)
print(recoError[counterError] - recoError[counterError - 1])
counterError += 1
# updating betaR and betaL
betaR = np.diag(np.diag(np.maximum(betaR *
((np.dot(np.dot(WM.T, SXR / np.maximum(hatSXR ** 2,
eps)),
HM.T)) /
(np.dot(np.dot(WM.T, 1 / np.maximum(hatSXR,
eps)),
HM.T))) ** (omega*.1), eps)))
betaL = np.diag(np.diag(np.maximum(betaL *
((np.dot(np.dot(WM.T, SXL / np.maximum(hatSXL ** 2,
eps)),
HM.T)) /
(np.dot(np.dot(WM.T, 1 / np.maximum(hatSXL,
eps)),
HM.T))) ** (omega*.1), eps)))
betaR = betaR / np.maximum(betaR + betaL, eps)
betaL = np.copy(np.eye(R) - betaR)
hatSXR = np.maximum(np.dot(np.dot(WM, betaR**2), HM), eps)
hatSXL = np.maximum(np.dot(np.dot(WM, betaL**2), HM), eps)
recoError[counterError] = ISDistortion(SXR, hatSXR) \
+ ISDistortion(SXL, hatSXL)
if verbose:
print("Reconstruction error difference after BETA : ",)
print(recoError[counterError] - recoError[counterError - 1])
counterError += 1
return betaR, betaL, HM, WM
def main():
import optparse
usage = "usage: %prog [options] inputAudioFile"
parser = optparse.OptionParser(usage)
# Name of the output files:
parser.add_option("-v", "--vocal-output-file",
dest="voc_output_file", type="string",
help="name of the audio output file for the estimated\n"\
"solo (vocal) part. \n"\
"If None, appends _lead to inputAudioFile.",
default=None)
parser.add_option("-m", "--music-output-file",
dest="mus_output_file", type="string",
help="name of the audio output file for the estimated\n"\
"music part.\n"\
"If None, appends _acc to inputAudioFile.",
default=None)
parser.add_option("-p", "--pitch-output-file",
dest="pitch_output_file", type="string",
help="name of the output file for the estimated pitches.\n"
"If None, appends _pitches to inputAudioFile",
default=None)
# Some more optional options:
parser.add_option("-d", "--with-display", dest="displayEvolution",
action="store_true",help="display the figures",
default=False)
parser.add_option("-q", "--quiet", dest="verbose",
action="store_false",
help="use to quiet all output verbose",
default=True)
parser.add_option("-n", "--dontseparate", dest="separateSignals",
action="store_false",
help="Trigger this option if you only desire to "+\
"estimate the melody",
default=True)
parser.add_option("--nb-iterations", dest="nbiter",
help="number of iterations", type="int",
default=30)
parser.add_option("--window-size", dest="windowSize", type="float",
default=0.04644,help="size of analysis windows, in s.")
parser.add_option("--Fourier-size", dest="fourierSize", type="int",
default=None,
help="size of Fourier transforms, "\
"in samples.")
parser.add_option("--hopsize", dest="hopsize", type="float",
default=0.0058,
help="size of the hop between analysis windows, in s.")
parser.add_option("--nb-accElements", dest="R", type="float",
default=40.0,
help="number of elements for the accompaniment.")
parser.add_option("--with-melody", dest="melody", type="string",
default=None,
help="provide the melody in a file named MELODY, "\
"with at each line: <time (s)><F0 (Hz)>.")
parser.add_option("--numAtomFilters", dest="P_numAtomFilters",
type="int", default=30,
help="Number of atomic filters - in WGAMMA.")
parser.add_option("--numFilters", dest="K_numFilters", type="int",
default=10,
help="Number of filters for decomposition - in WPHI")
parser.add_option("--min-F0-Freq", dest="minF0", type="float",
default=100.0,
help="Minimum of fundamental frequency F0.")
parser.add_option("--max-F0-Freq", dest="maxF0", type="float",
default=800.0,
help="Maximum of fundamental frequency F0.")
parser.add_option("--step-F0s", dest="stepNotes", type="int",
default=20,
help="Number of F0s in dictionary for each semitone.")
(options, args) = parser.parse_args()
if len(args) != 1:
parser.error("incorrect number of arguments, use option -h for help.")
displayEvolution = options.displayEvolution
if displayEvolution:
import matplotlib.pyplot as plt
import imageMatlab
## plt.rc('text', usetex=True)
plt.rc('image',cmap='jet') ## gray_r
plt.ion()
# Compulsory option: name of the input file:
inputAudioFile = args[0]
if inputAudioFile[-4:] != ".wav":
raise ValueError("File not WAV file? Only WAV format support, for now...")
if options.mus_output_file is None:
options.mus_output_file = inputAudioFile[:-4]+'_acc.wav'
if options.voc_output_file is None:
options.voc_output_file = inputAudioFile[:-4]+'_lead.wav'
if options.pitch_output_file is None:
options.pitch_output_file = inputAudioFile[:-4]+'_pitches.txt'
print("Writing the different following output files:")
print(" separated lead in", options.voc_output_file)
print(" separated accompaniment in", options.mus_output_file)
print(" separated lead + unvoc in", options.voc_output_file[:-4] + \
'_VUIMM.wav')
print(" separated acc - unvoc in", options.mus_output_file[:-4] + \
'_VUIMM.wav')
print(" estimated pitches in", options.pitch_output_file)
Fs, data = wav.read(inputAudioFile)
# data = np.double(data) / 32768.0 # makes data vary from -1 to 1
scaleData = 1.2 * data.max() # to rescale the data.
dataType = data.dtype
data = np.double(data) / scaleData # makes data vary from -1 to 1
is_stereo = True
if data.shape[0] == data.size: # data is multi-channel
print("The audio file is not stereo. Making stereo out of mono.")
print("(You could also try the older separateLead.py...)")
is_stereo = False
# data = np.vstack([data,data]).T
# raise ValueError("number of dimensions of the input not 2")
if is_stereo and data.shape[1] != 2:
print("The data is multichannel, but not stereo... \n")
print("Unfortunately this program does not scale well. Data is \n")
print("reduced to its 2 first channels.\n")
data = data[:,0:2]
# Processing the options:
windowSizeInSamples = int(nextpow2(np.round(options.windowSize * Fs)) )
hopsize = np.round(options.hopsize * Fs)
if hopsize != windowSizeInSamples/8:
#print "Overriding given hopsize to use 1/8th of window size"
#hopsize = windowSizeInSamples/8
warnings.warn("Chosen hopsize: "+str(hopsize)+\
", while windowsize: "+str(windowSizeInSamples))
if options.fourierSize is None:
NFT = windowSizeInSamples
else:
NFT = options.fourierSize
# number of iterations for each parameter estimation step:
niter = options.nbiter
# number of spectral shapes for the accompaniment
R = options.R
eps = 10 ** -9
if options.verbose:
print("Some parameter settings:")
print(" Size of analysis windows: ", windowSizeInSamples)
print(" Hopsize: ", hopsize)
print(" Size of Fourier transforms: ", NFT)
print(" Number of iterations to be done: ", niter)
print(" Number of elements in WM: ", R)
if is_stereo:
XR, F, N = stft(data[:,0], fs=Fs, hopsize=hopsize,
window=sinebell(windowSizeInSamples), nfft=NFT)
XL, F, N = stft(data[:,1], fs=Fs, hopsize=hopsize,
window=sinebell(windowSizeInSamples), nfft=NFT)
# SX is the power spectrogram:
## SXR = np.maximum(np.abs(XR) ** 2, 10 ** -8)
## SXL = np.maximum(np.abs(XL) ** 2, 10 ** -8)
#SXR = np.abs(XR) ** 2
#SXL = np.abs(XL) ** 2
SX = np.maximum((0.5*np.abs(XR+XL)) ** 2, eps)
else: # data is mono
X, F, N = stft(data, fs=Fs, hopsize=hopsize,
window=sinebell(windowSizeInSamples), nfft=NFT)
SX = np.maximum(np.abs(X) ** 2, eps)
del data, F, N
# TODO: also process these as options:
# minimum and maximum F0 in glottal source spectra dictionary
minF0 = options.minF0
maxF0 = options.maxF0
F, N = SX.shape
stepNotes = options.stepNotes # this is the number of F0s within one semitone
K = options.K_numFilters # number of spectral shapes for the filter part
P = options.P_numAtomFilters # number of elements in dictionary of smooth filters
chirpPerF0 = 1 # number of chirped spectral shapes between each F0
# this feature should be further studied before
# we find a good way of doing that.
# Create the harmonic combs, for each F0 between minF0 and maxF0:
F0Table, WF0 = \
generate_WF0_chirped(minF0, maxF0, Fs, Nfft=NFT, \
stepNotes=stepNotes, \
lengthWindow=windowSizeInSamples, Ot=0.25, \
perF0=chirpPerF0, \
depthChirpInSemiTone=.15, loadWF0=True,\
analysisWindow='sinebell')
WF0 = WF0[0:F, :] # ensure same size as SX
NF0 = F0Table.size # number of harmonic combs
# Normalization:
WF0 = WF0 / np.outer(np.ones(F), np.amax(WF0, axis=0))
# Create the dictionary of smooth filters, for the filter part of
# the lead isntrument:
WGAMMA = generateHannBasis(F, NFT, Fs=Fs, frequencyScale='linear', \
numberOfBasis=P, overlap=.75)
if displayEvolution:
plt.figure(1);plt.clf()
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel(r'Frame number $n$', fontsize=16)
plt.ylabel(r'Leading source number $u$', fontsize=16)
plt.ion()
# plt.show()
## the following seems superfluous if mpl's backend is macosx...
## raw_input("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"\
## "!! Press Return to resume the program. !!\n"\
## "!! Be sure that the figure has been !!\n"\
## "!! already displayed, so that the !!\n"\
## "!! evolution of HF0 will be visible. !!\n"\
## "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
if options.melody is None:
## section to estimate the melody, on monophonic algo:
# First round of parameter estimation:
HGAMMA, HPHI, HF0, HM, WM, recoError1 = SIMM.SIMM(
# the data to be fitted to:
SX,
# the basis matrices for the spectral combs
WF0,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K, numberOfAccompanimentSpectralShapes=R,
# putting only 2 elements in accompaniment for a start...
# if any, initial amplitude matrices for
HGAMMA0=None, HPHI0=None,
HF00=None,
WM0=None, HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter, updateRulePower=1.,
stepNotes=stepNotes,
lambdaHF0 = 0.0 / (1.0 * SX.max()), alphaHF0=0.9,
verbose=options.verbose, displayEvolution=displayEvolution)
if displayEvolution:
h2 = plt.figure(2);plt.clf();
imageMatlab.imageM(20 * np.log10(HF0))
matMax = (20 * np.log10(HF0)).max()
matMed = np.median(20 * np.log10(HF0))
plt.clim([matMed - 100, matMax])
# Viterbi decoding to estimate the predominant fundamental
# frequency line
# create transition probability matrix - adhoc parameter 'scale'
# TODO: use "learned" parameter scale (NB: after many trials,
# provided scale and parameterization seems robust)
scale = 1.0
transitions = np.exp(-np.floor(np.arange(0,NF0) / stepNotes) * scale)
cutoffnote = 2 * 5 * stepNotes
transitions[cutoffnote:] = transitions[cutoffnote - 1]
transitionMatrixF0 = np.zeros([NF0 + 1, NF0 + 1]) # toeplitz matrix
b = np.arange(NF0)
transitionMatrixF0[0:NF0, 0:NF0] = \
transitions[\
np.array(np.abs(np.outer(np.ones(NF0), b) \
- np.outer(b, np.ones(NF0))), dtype=int)]
pf_0 = transitions[cutoffnote - 1] * 10 ** (-90)
p0_0 = transitions[cutoffnote - 1] * 10 ** (-100)
p0_f = transitions[cutoffnote - 1] * 10 ** (-80)
transitionMatrixF0[0:NF0, NF0] = pf_0
transitionMatrixF0[NF0, 0:NF0] = p0_f
transitionMatrixF0[NF0, NF0] = p0_0
sumTransitionMatrixF0 = np.sum(transitionMatrixF0, axis=1)
transitionMatrixF0 = transitionMatrixF0 \
/ np.outer(sumTransitionMatrixF0, \
np.ones(NF0 + 1))
# prior probabilities, and setting the array for Viterbi tracking:
priorProbabilities = 1 / (NF0 + 1.0) * np.ones([NF0 + 1])
logHF0 = np.zeros([NF0 + 1, N])
normHF0 = np.amax(HF0, axis=0)
barHF0 = np.array(HF0)
logHF0[0:NF0, :] = np.log(barHF0)
logHF0[0:NF0, normHF0==0] = np.amin(logHF0[logHF0>-np.Inf])
logHF0[NF0, :] = np.maximum(np.amin(logHF0[logHF0>-np.Inf]),-100)
indexBestPath = viterbiTrackingArray(\
logHF0, np.log(priorProbabilities),
np.log(transitionMatrixF0), verbose=options.verbose)
if displayEvolution:
h2.hold(True)
plt.plot(indexBestPath, '-b')
h2.hold(False)
plt.axis('tight')
del logHF0
# detection of silences:
# computing the melody restricted F0 amplitude matrix HF00
# (which will be used as initial HF0 for further algo):
HF00 = np.zeros([NF0 * chirpPerF0, N])
scopeAllowedHF0 = 2.0 / 1.0
# computing indices for and around the melody indices,
# dim1index are indices along axis 0, and dim2index along axis 1
# of HF0:
# TODO: use numpy broadcasting to make this "clearer" (if possible...)
dim1index = np.array(\
np.maximum(\
np.minimum(\
np.outer(chirpPerF0 * indexBestPath,
np.ones( int(chirpPerF0 * (2 * np.floor(stepNotes / scopeAllowedHF0) + 1)) )) \
+ np.outer(np.ones(N),
np.arange(-chirpPerF0 * np.floor(stepNotes / scopeAllowedHF0),
chirpPerF0 * (np.floor(stepNotes / scopeAllowedHF0) + 1))),
chirpPerF0 * NF0 - 1),
0),
dtype=int).reshape(1, int(N * chirpPerF0 * (2 * np.floor(stepNotes / scopeAllowedHF0) + 1)))
dim2index = np.outer(np.arange(N),
np.ones( int(chirpPerF0 * (2 * np.floor(stepNotes / scopeAllowedHF0) + 1)), dtype=int)\
).reshape(1, int(N * chirpPerF0 * (2 * np.floor(stepNotes / scopeAllowedHF0) + 1)))
HF00[dim1index, dim2index] = HF0[dim1index, dim2index]# HF0.max()
HF00[:, indexBestPath == (NF0 - 1)] = 0.0
HF00[:, indexBestPath == 0] = 0.0
# remove frames with less than (100 thres_energy) % of total energy.
thres_energy = 0.000584
SF0 = np.maximum(np.dot(WF0, HF00), eps)
SPHI = np.maximum(np.dot(WGAMMA, np.dot(HGAMMA, HPHI)), eps)
SM = np.maximum(np.dot(WM, HM), eps)
hatSX = np.maximum(SPHI * SF0 + SM, eps)
energyMel = np.sum((((SPHI * SF0)/hatSX)**2) * SX, axis=0)
energyMelSorted = np.sort(energyMel)
energyMelCumul = np.cumsum(energyMelSorted)
energyMelCumulNorm = energyMelCumul / max(energyMelCumul[-1], eps)
# normalized to the maximum of energy:
# expressed in 0.01 times the percentage
ind_999 = np.nonzero(energyMelCumulNorm>thres_energy)[0][0]
if ind_999 is None:
ind_999 = N
melNotPresent = (energyMel <= energyMelCumulNorm[ind_999])
indexBestPath[melNotPresent] = 0
else:
## take the provided melody line:
# load melody from file:
melodyFromFile = np.loadtxt(options.melody)
sizeProvidedMel = melodyFromFile.shape
if len(sizeProvidedMel) == 1:
print("The melody should be provided as <Time (s)><F0 (Hz)>.")
raise ValueError("Bad melody format")
melTimeStamps = melodyFromFile[:,0] # + 1024 / np.double(Fs)
melFreqHz = melodyFromFile[:,1]
if minF0 > melFreqHz[melFreqHz>40.0].min() or maxF0 < melFreqHz.max():
minF0 = melFreqHz[melFreqHz>40.0].min() *.97
maxF0 = np.maximum(melFreqHz.max()*1.03, 2*minF0 * 1.03)
print("Recomputing the source basis for ")
print("minF0 = ", minF0, "Hz and maxF0 = ", maxF0, "Hz.")
# Create the harmonic combs, for each F0 between minF0 and maxF0:
F0Table, WF0 = \
generate_WF0_chirped(minF0, maxF0, Fs, Nfft=NFT, \
stepNotes=stepNotes, \
lengthWindow=windowSizeInSamples,
Ot=0.25, \
perF0=chirpPerF0, \
depthChirpInSemiTone=.15)
WF0 = WF0[0:F, :] # ensure same size as SX
NF0 = F0Table.size # number of harmonic combs
# Normalization:
WF0 = WF0 / np.outer(np.ones(F), np.amax(WF0, axis=0))
sigTimeStamps = np.arange(N) * hopsize / np.double(Fs)
distMatTimeStamps = np.abs(np.outer(np.ones(sizeProvidedMel[0]),
sigTimeStamps) -
np.outer(melTimeStamps, np.ones(N)))
minDistTimeStamps = distMatTimeStamps.argmin(axis=0)
f0BestPath = melFreqHz[minDistTimeStamps]
distMatF0 = np.abs(np.outer(np.ones(NF0), f0BestPath) -
np.outer(F0Table, np.ones(N)))
indexBestPath = distMatF0.argmin(axis=0)
# setting silences to 0, with tolerance = 1/2 window length
indexBestPath[distMatTimeStamps[minDistTimeStamps,range(N)] >= \
0.5 * options.windowSize] = 0
indexBestPath[f0BestPath<=0] = 0
freqMelody = F0Table[np.array(indexBestPath,dtype=int)]
freqMelody[indexBestPath==0] = - freqMelody[indexBestPath==0]
np.savetxt(options.pitch_output_file,
np.array([np.arange(N) * hopsize / np.double(Fs),
freqMelody]).T)
# If separation is required:
if options.separateSignals:
# Second round of parameter estimation, with specific
# initial HF00:
HF00 = np.zeros([NF0 * chirpPerF0, N])
scopeAllowedHF0 = 2.0 / 1.0
# indexes for HF00:
# TODO: reprogram this with a 'where'?...
dim1index = np.array(\
np.maximum(\
np.minimum(\
np.outer(chirpPerF0 * indexBestPath,
np.ones( int(chirpPerF0 * (2 * np.floor(stepNotes / scopeAllowedHF0) + 1) ))) \
+ np.outer(np.ones(N),
np.arange(-chirpPerF0 * np.floor(stepNotes / scopeAllowedHF0),
chirpPerF0 * (np.floor(stepNotes / scopeAllowedHF0) + 1))),
chirpPerF0 * NF0 - 1),
0),
dtype=int)
dim1index = dim1index[indexBestPath!=0,:]
## dim1index = dim1index.reshape(1, N * chirpPerF0 \
## * (2 * np.floor(stepNotes / scopeAllowedHF0) \
## + 1))
dim1index = dim1index.reshape(1,dim1index.size)
dim2index = np.outer(np.arange(N), np.ones( int(chirpPerF0 * (2 * np.floor(stepNotes / scopeAllowedHF0) + 1)), dtype=int) )
dim2index = dim2index[indexBestPath!=0,:]
dim2index = dim2index.reshape(1,dim2index.size)
## dim2index.reshape(1, N * chirpPerF0 \
## * (2 * np.floor(stepNotes \
## / scopeAllowedHF0) \
## + 1))
HF00[dim1index, dim2index] = 1 # HF0.max()
HF00[:, indexBestPath == (NF0 - 1)] = 0.0
HF00[:, indexBestPath == 0] = 0.0
WF0effective = WF0
HF00effective = HF00
if options.melody is None:
del HF0, HGAMMA, HPHI, HM, WM, HF00
if is_stereo:
del SX
SXR = np.maximum(np.abs(XR) ** 2, eps)
SXL = np.maximum(np.abs(XL) ** 2, eps)
alphaR, alphaL, HGAMMA, HPHI, HF0, \
betaR, betaL, HM, WM, recoError2 = SIMM.Stereo_SIMM(
# the data to be fitted to:
SXR, SXL,
# the basis matrices for the spectral combs
WF0effective,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K, numberOfAccompanimentSpectralShapes=R,
# if any, initial amplitude matrices for
HGAMMA0=None, HPHI0=None,
HF00=HF00effective,
WM0=None, HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter, updateRulePower=1.0,
stepNotes=stepNotes,
lambdaHF0 = 0.0 / (1.0 * SXR.max()), alphaHF0=0.9,
verbose=options.verbose, displayEvolution=displayEvolution)
WPHI = np.dot(WGAMMA, HGAMMA)
SPHI = np.dot(WPHI, HPHI)
SF0 = np.dot(WF0effective, HF0)
hatSXR = (alphaR**2) * SF0 * SPHI + np.dot(np.dot(WM, betaR**2),HM)
hatSXL = (alphaL**2) * SF0 * SPHI + np.dot(np.dot(WM, betaL**2),HM)
hatVR = (alphaR**2) * SPHI * SF0 / hatSXR * XR
vestR = istft(hatVR, hopsize=int(hopsize), nfft=int(NFT), window=sinebell(windowSizeInSamples)) / 4.0
hatVR = (alphaL**2) * SPHI * SF0 / hatSXL * XL
vestL = istft(hatVR, hopsize=int(hopsize), nfft=int(NFT), window=sinebell(windowSizeInSamples)) / 4.0
#scikits.audiolab.wavwrite(np.array([vestR,vestL]).T, \
# options.voc_output_file, Fs)
vestR = np.array(np.round(vestR*scaleData), dtype=dataType)
vestL = np.array(np.round(vestL*scaleData), dtype=dataType)
wav.write(options.voc_output_file, Fs, \
np.array([vestR,vestL]).T)
#wav.write(options.voc_output_file, Fs, \
# np.int16(32768.0 * np.array([vestR,vestL]).T))
hatMR = (np.dot(np.dot(WM,betaR ** 2),HM)) / hatSXR * XR
mestR = istft(hatMR, hopsize=int(hopsize), nfft=int(NFT), window=sinebell(windowSizeInSamples)) / 4.0
hatMR = (np.dot(np.dot(WM,betaL ** 2),HM)) / hatSXL * XL
mestL = istft(hatMR, hopsize=int(hopsize), nfft=int(NFT), window=sinebell(windowSizeInSamples)) / 4.0
#scikits.audiolab.wavwrite(np.array([mestR,mestL]).T, \
# options.mus_output_file, Fs)
mestR = np.array(np.round(mestR*scaleData), dtype=dataType)
mestL = np.array(np.round(mestL*scaleData), dtype=dataType)
wav.write(options.mus_output_file, Fs, \
np.array([mestR,mestL]).T)
#wav.write(options.mus_output_file, Fs, \
# np.int16(32768.0 * np.array([mestR,mestL]).T))
del hatMR, mestL, vestL, vestR, mestR, hatVR, hatSXR, hatSXL, SPHI, SF0
# adding the unvoiced part in the source basis:
WUF0 = np.hstack([WF0, np.ones([WF0.shape[0], 1])])
HUF0 = np.vstack([HF0, np.ones([1, HF0.shape[1]])])
## HUF0[-1,:] = HF0.sum(axis=0) # should we do this?
alphaR, alphaL, HGAMMA, HPHI, HF0, \
betaR, betaL, HM, WM, recoError3 = SIMM.Stereo_SIMM(
# the data to be fitted to:
SXR, SXL,
# the basis matrices for the spectral combs
WUF0,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K, numberOfAccompanimentSpectralShapes=R,
# if any, initial amplitude matrices for
HGAMMA0=HGAMMA, HPHI0=HPHI,
HF00=HUF0,
WM0=None,#WM,
HM0=None,#HM,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter, updateRulePower=1.0,
stepNotes=stepNotes,
lambdaHF0 = 0.0 / (1.0 * SXR.max()), alphaHF0=0.9,
verbose=options.verbose, displayEvolution=displayEvolution,
updateHGAMMA=False)
WPHI = np.dot(WGAMMA, HGAMMA)
SPHI = np.dot(WPHI, HPHI)
SF0 = np.dot(WUF0, HF0)
hatSXR = (alphaR**2) * SF0 * SPHI + np.dot(np.dot(WM, betaR**2),HM)
hatSXL = (alphaL**2) * SF0 * SPHI + np.dot(np.dot(WM, betaL**2),HM)
hatVR = (alphaR**2) * SPHI * SF0 / hatSXR * XR
vestR = istft(hatVR, hopsize=int(hopsize), nfft=int(NFT), window=sinebell(windowSizeInSamples)) / 4.0
hatVR = (alphaL**2) * SPHI * SF0 / hatSXL * XL
vestL = istft(hatVR, hopsize=int(hopsize), nfft=int(NFT), window=sinebell(windowSizeInSamples)) / 4.0
outputFileName = options.voc_output_file[:-4] + '_VUIMM.wav'
vestR = np.array(np.round(vestR*scaleData), dtype=dataType)
vestL = np.array(np.round(vestL*scaleData), dtype=dataType)
wav.write(outputFileName, Fs, \
np.array([vestR,vestL]).T)
hatMR = (np.dot(np.dot(WM,betaR ** 2),HM)) / hatSXR * XR
mestR = istft(hatMR, hopsize=int(hopsize), nfft=int(NFT), window=sinebell(windowSizeInSamples)) / 4.0
hatMR = (np.dot(np.dot(WM,betaL ** 2),HM)) / hatSXL * XL
mestL = istft(hatMR, hopsize=int(hopsize), nfft=int(NFT), window=sinebell(windowSizeInSamples)) / 4.0
outputFileName = options.mus_output_file[:-4] + '_VUIMM.wav'
mestR = np.array(np.round(mestR*scaleData), dtype=dataType)
mestL = np.array(np.round(mestL*scaleData), dtype=dataType)
wav.write(outputFileName, Fs, \
np.array([mestR,mestL]).T)
else:
# running on monophonic data:
HGAMMA, HPHI, HF0, HM, WM, recoError1 = SIMM.SIMM(
# the data to be fitted to:
SX,
# the basis matrices for the spectral combs
WF0effective,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K, numberOfAccompanimentSpectralShapes=R,
# putting only 2 elements in accompaniment for a start...
# if any, initial amplitude matrices for
HGAMMA0=None, HPHI0=None,
HF00=HF00effective,
WM0=None, HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter, updateRulePower=1.,
stepNotes=stepNotes,
lambdaHF0 = 0.0 / (1.0 * SX.max()), alphaHF0=0.9,
verbose=options.verbose, displayEvolution=displayEvolution)
WPHI = np.dot(WGAMMA, HGAMMA)
SPHI = np.dot(WPHI, HPHI)
SF0 = np.dot(WF0effective, HF0)
SM = np.dot(WM,HM)
hatSX = SF0 * SPHI + SM
hatV = SPHI * SF0 / hatSX * X
vest = istft(hatV, hopsize=int(hopsize), nfft=int(NFT), window=sinebell(windowSizeInSamples)) / 4.0
vest = np.array(np.round(vest*scaleData), dtype=dataType)
wav.write(options.voc_output_file, Fs, vest)
hatM = SM / hatSX * X
mest = istft(hatM, hopsize=int(hopsize), nfft=int(NFT), window=sinebell(windowSizeInSamples)) / 4.0
mest = np.array(np.round(mest*scaleData), dtype=dataType)
wav.write(options.mus_output_file, Fs, mest)
del hatM, vest, mest, hatV, hatSX, SPHI, SF0
# adding the unvoiced part in the source basis:
WUF0 = np.hstack([WF0, np.ones([WF0.shape[0], 1])])
HUF0 = np.vstack([HF0, np.ones([1, HF0.shape[1]])])
## HUF0[-1,:] = HF0.sum(axis=0) # should we do this?
HGAMMA, HPHI, HF0, HM, WM, recoError1 = SIMM.SIMM(
# the data to be fitted to:
SX,
# the basis matrices for the spectral combs
WUF0,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K, numberOfAccompanimentSpectralShapes=R,
# putting only 2 elements in accompaniment for a start...
# if any, initial amplitude matrices for
HGAMMA0=HGAMMA, HPHI0=HPHI,
HF00=HUF0,
WM0=None, HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter, updateRulePower=1.,
stepNotes=stepNotes,
lambdaHF0 = 0.0 / (1.0 * SX.max()), alphaHF0=0.9,
verbose=options.verbose, displayEvolution=displayEvolution,
updateHGAMMA=False)
WPHI = np.dot(WGAMMA, HGAMMA)
SPHI = np.dot(WPHI, HPHI)
SF0 = np.dot(WUF0, HF0)
SM = np.dot(WM,HM)
hatSX = SF0 * SPHI + SM
hatV = SPHI * SF0 / hatSX * X
vest = istft(hatV, hopsize=int(hopsize), nfft=int(NFT), window=sinebell(windowSizeInSamples)) / 4.0
vest = np.array(np.round(vest*scaleData), dtype=dataType)
outputFileName = options.voc_output_file[:-4] + '_VUIMM.wav'
wav.write(outputFileName, Fs, vest)
hatM = SM / hatSX * X
mest = istft(hatM, hopsize=int(hopsize), nfft=int(NFT), window=sinebell(windowSizeInSamples)) / 4.0
mest = np.array(np.round(mest*scaleData), dtype=dataType)
outputFileName = options.mus_output_file[:-4] + '_VUIMM.wav'
wav.write(outputFileName, Fs, mest)
if displayEvolution:
plt.close('all')
print("Done!")
def stereo_NMF(SXR, SXL,
numberOfAccompanimentSpectralShapes,
WM0=None, HM0=None,
numberOfIterations=50, updateRulePower=1.0,
verbose=False, displayEvolution=False):
eps = 10 ** (-20)
if displayEvolution:
import matplotlib.pyplot as plt
from imageMatlab import imageM
plt.ion()
print "Is the display interactive? ", plt.isinteractive()
R = numberOfAccompanimentSpectralShapes
omega = updateRulePower
F, N = SXR.shape
if (F, N) != SXL.shape:
print "The input STFT matrices do not have the same dimension.\n"
print "Please check what happened..."
raise ValueError("Dimension of STFT matrices must be the same.")
if HM0 is None:
HM0 = np.abs(randn(R, N))
else:
if np.array(HM0).shape[0] == R and np.array(HM0).shape[1] == N:
HM0 = np.array(HM0)
else:
print "Wrong dimensions for given HM0, \n"
print "random initialization used instead"
HM0 = np.abs(randn(R, N))
HM = np.copy(HM0)
if WM0 is None:
WM0 = np.abs(randn(F, R))
else:
if np.array(WM0).shape[0] == F and np.array(WM0).shape[1] == R:
WM0 = np.array(WM0)
else:
print "Wrong dimensions for given WM0, \n"
print "random initialization used instead"
WM0 = np.abs(randn(F, R))
WM = np.copy(WM0)
betaR = np.diag(np.random.rand(R))
betaL = np.eye(R) - betaR
hatSXR = np.maximum(np.dot(np.dot(WM, betaR**2), HM), eps)
hatSXL = np.maximum(np.dot(np.dot(WM, betaL**2), HM), eps)
# temporary matrices
tempNumFbyN = np.zeros([F, N])
tempDenFbyN = np.zeros([F, N])
recoError = np.zeros([numberOfIterations * 3 + 1])
recoError[0] = ISDistortion(SXR, hatSXR) + ISDistortion(SXL, hatSXL)
if verbose:
print "Reconstruction error at beginning: ", recoError[0]
counterError = 1
if displayEvolution:
h1 = plt.figure(1)
for n in np.arange(numberOfIterations):
# order of re-estimation: HF0, HPHI, HM, HGAMMA, WM
if verbose:
print "iteration ", n, " over ", numberOfIterations
if displayEvolution:
h1.clf()
imageM(db(hatSXR))
plt.clim([np.amax(db(hatSXR))-100, np.amax(db(hatSXR))])
plt.draw()
# updating HM
HM = HM * \
((np.dot(np.dot((betaR**2), WM.T), SXR /
np.maximum(hatSXR ** 2, eps)) +
np.dot(np.dot((betaL**2), WM.T), SXL /
np.maximum(hatSXL ** 2, eps))
) /
np.maximum(np.dot(np.dot((betaR**2), WM.T), 1 /
np.maximum(hatSXR, eps)) +
np.dot(np.dot((betaL**2), WM.T), 1 /
np.maximum(hatSXL, eps)),
eps)) ** omega
hatSXR = np.maximum(np.dot(np.dot(WM, betaR**2),HM), eps)
hatSXL = np.maximum(np.dot(np.dot(WM, betaL**2),HM), eps)
recoError[counterError] = ISDistortion(SXR, hatSXR) \
+ ISDistortion(SXL, hatSXL)
if verbose:
print "Reconstruction error difference after HM : ",\
recoError[counterError] - recoError[counterError - 1]
counterError += 1
# updating WM
WM = WM * \
((np.dot(SXR / np.maximum(hatSXR ** 2, eps),
np.dot(HM.T, betaR ** 2)) +
np.dot(SXL / np.maximum(hatSXL ** 2, eps),
np.dot(HM.T, betaL ** 2))
) /
(np.dot(1 / np.maximum(hatSXR, eps),
np.dot(HM.T, betaR ** 2)) +
np.dot(1 / np.maximum(hatSXL, eps),
np.dot(HM.T, betaL ** 2))
)) ** omega
sumWM = np.sum(WM, axis=0)
WM[:, sumWM>0] = (WM[:, sumWM>0] /
np.outer(np.ones(F),sumWM[sumWM>0]))
HM = HM * np.outer(sumWM, np.ones(N))
hatSXR = np.maximum(np.dot(np.dot(WM, betaR**2), HM), eps)
hatSXL = np.maximum(np.dot(np.dot(WM, betaL**2), HM), eps)
recoError[counterError] = ISDistortion(SXR, hatSXR) \
+ ISDistortion(SXL, hatSXL)
if verbose:
print "Reconstruction error difference after WM : ",
print recoError[counterError] - recoError[counterError - 1]
counterError += 1
# updating betaR and betaL
betaR = np.diag(np.diag(np.maximum(betaR *
((np.dot(np.dot(WM.T, SXR / np.maximum(hatSXR ** 2,
eps)),
HM.T)) /
(np.dot(np.dot(WM.T, 1 / np.maximum(hatSXR,
eps)),
HM.T))) ** (omega*.1), eps)))
betaL = np.diag(np.diag(np.maximum(betaL *
((np.dot(np.dot(WM.T, SXL / np.maximum(hatSXL ** 2,
eps)),
HM.T)) /
(np.dot(np.dot(WM.T, 1 / np.maximum(hatSXL,
eps)),
HM.T))) ** (omega*.1), eps)))
betaR = betaR / np.maximum(betaR + betaL, eps)
betaL = np.copy(np.eye(R) - betaR)
hatSXR = np.maximum(np.dot(np.dot(WM, betaR**2), HM), eps)
hatSXL = np.maximum(np.dot(np.dot(WM, betaL**2), HM), eps)
recoError[counterError] = ISDistortion(SXR, hatSXR) \
+ ISDistortion(SXL, hatSXL)
if verbose:
print "Reconstruction error difference after BETA : ",
print recoError[counterError] - recoError[counterError - 1]
counterError += 1
return betaR, betaL, HM, WM
def SIMM(# the data to be fitted to:
SX,
# the basis matrices for the spectral combs
WF0,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=4, numberOfAccompanimentSpectralShapes=10,
# if any, initial amplitude matrices for
HGAMMA0=None, HPHI0=None,
HF00=None,
WM0=None, HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=1000, updateRulePower=1.0,
stepNotes=4,
lambdaHF0=0.00,alphaHF0=0.99,
displayEvolution=False, verbose=True, makeMovie=False,
updateHGAMMA=True,
computeISDistortion=False):
"""
HGAMMA, HPHI, HF0, HM, WM, recoError =
SIMM(SX, WF0, WGAMMA, numberOfFilters=4,
numberOfAccompanimentSpectralShapes=10, HGAMMA0=None, HPHI0=None,
HF00=None, WM0=None, HM0=None, numberOfIterations=1000,
updateRulePower=1.0, stepNotes=4,
lambdaHF0=0.00, alphaHF0=0.99, displayEvolution=False,
verbose=True)
Implementation of the Smooth-filters Instantaneous Mixture Model
(SIMM). This model can be used to estimate the main melody of a
song, and separate the lead voice from the accompaniment, provided
that the basis WF0 is constituted of elements associated to
particular pitches.
Inputs:
SX
the F x N power spectrogram to be approximated.
F is the number of frequency bins, while N is the number of
analysis frames
WF0
the F x NF0 basis matrix containing the NF0 source elements
WGAMMA
the F x P basis matrix of P smooth elementary filters
numberOfFilters
the number of filters K to be considered
numberOfAccompanimentSpectralShapes
the number of spectral shapes R for the accompaniment
HGAMMA0
the P x K decomposition matrix of WPHI on WGAMMA
HPHI0
the K x N amplitude matrix of the filter part of the lead
instrument
HF00
the NF0 x N amplitude matrix for the source part of the lead
instrument
WM0
the F x R the matrix for spectral shapes of the
accompaniment
HM0
the R x N amplitude matrix associated with each of the R
accompaniment spectral shapes
numberOfIterations
the number of iterations for the estimatino algorithm
updateRulePower
the power to which the multiplicative gradient is elevated to
stepNotes
the number of elements in WF0 per semitone. stepNotes=4 means
that there are 48 elements per octave in WF0.
lambdaHF0
Lagrangian multiplier for the octave control
alphaHF0
parameter that controls how much influence a lower octave
can have on the upper octave's amplitude.
Outputs:
HGAMMA
the estimated P x K decomposition matrix of WPHI on WGAMMA
HPHI
the estimated K x N amplitude matrix of the filter part
HF0
the estimated NF0 x N amplitude matrix for the source part
HM
the estimated R x N amplitude matrix for the accompaniment
WM
the estimate F x R spectral shapes for the accompaniment
recoError
the successive values of the Itakura Saito divergence
between the power spectrogram and the spectrogram
computed thanks to the updated estimations of the matrices.
Please also refer to the following article for more details about
the algorithm within this function, as well as the meaning of the
different matrices that are involved:
J.-L. Durrieu, G. Richard, B. David and C. Fevotte
Source/Filter Model for Unsupervised Main Melody
Extraction From Polyphonic Audio Signals
IEEE Transactions on Audio, Speech and Language Processing
Vol. 18, No. 3, March 2010
"""
eps = 10 ** (-20)
if displayEvolution:
import matplotlib.pyplot as plt
from imageMatlab import imageM
plt.ion()
print("Is the display interactive? ", plt.isinteractive())
# renamed for convenience:
K = numberOfFilters
R = int(numberOfAccompanimentSpectralShapes)
omega = updateRulePower
F, N = SX.shape
Fwf0, NF0 = WF0.shape
Fwgamma, P = WGAMMA.shape
# Checking the sizes of the matrices
if Fwf0 != F:
return False # A REVOIR!!!
if HGAMMA0 is None:
HGAMMA0 = np.abs(randn(P, K))
else:
if not(isinstance(HGAMMA0,np.ndarray)): # default behaviour
HGAMMA0 = np.array(HGAMMA0)
Phgamma0, Khgamma0 = HGAMMA0.shape
if Phgamma0 != P or Khgamma0 != K:
print("Wrong dimensions for given HGAMMA0, \n")
print("random initialization used instead")
HGAMMA0 = np.abs(randn(P, K))
HGAMMA = np.copy(HGAMMA0)
if HPHI0 is None: # default behaviour
HPHI = np.abs(randn(K, N))
else:
Khphi0, Nhphi0 = np.array(HPHI0).shape
if Khphi0 != K or Nhphi0 != N:
print("Wrong dimensions for given HPHI0, \n")
print("random initialization used instead")
HPHI = np.abs(randn(K, N))
else:
HPHI = np.copy(np.array(HPHI0))
if HF00 is None:
HF00 = np.abs(randn(NF0, N))
else:
if np.array(HF00).shape[0] == NF0 and np.array(HF00).shape[1] == N:
HF00 = np.array(HF00)
else:
print("Wrong dimensions for given HF00, \n")
print("random initialization used instead")
HF00 = np.abs(randn(NF0, N))
HF0 = np.copy(HF00)
if HM0 is None:
HM0 = np.abs(randn(R, N))
else:
if np.array(HM0).shape[0] == R and np.array(HM0).shape[1] == N:
HM0 = np.array(HM0)
else:
print("Wrong dimensions for given HM0, \n")
print("random initialization used instead")
HM0 = np.abs(randn(R, N))
HM = np.copy(HM0)
if WM0 is None:
WM0 = np.abs(randn(F, R))
else:
if np.array(WM0).shape[0] == F and np.array(WM0).shape[1] == R:
WM0 = np.array(WM0)
else:
print("Wrong dimensions for given WM0, \n")
print("random initialization used instead")
WM0 = np.abs(randn(F, R))
WM = np.copy(WM0)
# Iterations to estimate the SIMM parameters:
WPHI = np.dot(WGAMMA, HGAMMA)
SF0 = np.dot(WF0, HF0)
SPHI = np.dot(WPHI, HPHI)
SM = np.dot(WM, HM)
hatSX = SF0 * SPHI + SM
## SX = SX + np.abs(randn(F, N)) ** 2
# should not need this line
# which ensures that data is not
# 0 everywhere.
# temporary matrices
tempNumFbyN = np.zeros([F, N])
tempDenFbyN = np.zeros([F, N])
# Array containing the reconstruction error after the update of each
# of the parameter matrices:
recoError = np.zeros([numberOfIterations * 5 * 2 + NF0 * 2 + 1])
recoError[0] = ISDistortion(SX, hatSX)
if verbose:
print("Reconstruction error at beginning: ", recoError[0])
counterError = 1
if displayEvolution:
h1 = plt.figure(1)
if makeMovie:
dirName = 'tmp%s/' %time.strftime("%Y%m%d%H%M%S")
os.system('mkdir %s' %dirName)
# Main loop for multiplicative updating rules:
for n in np.arange(numberOfIterations):
# order of re-estimation: HF0, HPHI, HM, HGAMMA, WM
if verbose:
print("iteration ", n, " over ", numberOfIterations)
if displayEvolution:
h1.clf();imageM(db(HF0));
plt.clim([np.amax(db(HF0))-100, np.amax(db(HF0))]);plt.draw();
## h1.clf();
## imageM(HF0 * np.outer(np.ones([NF0, 1]),
## 1 / (HF0.max(axis=0))));
if makeMovie:
filename = dirName + '%04d' % n + '.png'
plt.savefig(filename, dpi=100)
# updating HF0:
tempNumFbyN = (SPHI * SX) / np.maximum(hatSX ** 2, eps)
tempDenFbyN = SPHI / np.maximum(hatSX, eps)
# This to enable octave control
HF0[np.arange(12 * stepNotes, NF0), :] \
= HF0[np.arange(12 * stepNotes, NF0), :] \
* (np.dot(WF0[:, np.arange(12 * stepNotes,
NF0)].T, tempNumFbyN) \
/ np.maximum(
np.dot(WF0[:, np.arange(12 * stepNotes, NF0)].T,
tempDenFbyN) \
+ lambdaHF0 * (- (alphaHF0 - 1.0) \
/ np.maximum(HF0[
np.arange(12 * stepNotes, NF0), :], eps) \
+ HF0[
np.arange(NF0 - 12 * stepNotes), :]),
eps)) ** omega
HF0[np.arange(12 * stepNotes), :] \
= HF0[np.arange(12 * stepNotes), :] \
* (np.dot(WF0[:, np.arange(12 * stepNotes)].T,
tempNumFbyN) /
np.maximum(
np.dot(WF0[:, np.arange(12 * stepNotes)].T,
tempDenFbyN), eps)) ** omega
### normal update rules without checking octaves:
##HF0 = HF0 * (np.dot(WF0.T, tempNumFbyN) /
## np.maximum(np.dot(WF0.T, tempDenFbyN), eps)) ** omega
SF0 = np.maximum(np.dot(WF0, HF0),eps)
hatSX = np.maximum(SF0 * SPHI + SM,eps)
if computeISDistortion:
recoError[counterError] = ISDistortion(SX, hatSX)
if verbose:
print("Reconstruction error difference after HF0 : ",)
print(recoError[counterError] - recoError[counterError - 1])
counterError += 1
# updating HPHI
tempNumFbyN = (SF0 * SX) / np.maximum(hatSX ** 2, eps)
tempDenFbyN = SF0 / np.maximum(hatSX, eps)
HPHI = HPHI * (np.dot(WPHI.T, tempNumFbyN) / \
np.maximum(np.dot(WPHI.T, tempDenFbyN), eps)) ** omega
sumHPHI = np.sum(HPHI, axis=0)
HPHI[:, sumHPHI>0] = HPHI[:, sumHPHI>0] / \
np.outer(np.ones(K), sumHPHI[sumHPHI>0])
HF0 = HF0 * np.outer(np.ones(NF0), sumHPHI)
SF0 = np.maximum(np.dot(WF0, HF0), eps)
SPHI = np.maximum(np.dot(WPHI, HPHI), eps)
hatSX = np.maximum(SF0 * SPHI + SM, eps)
if computeISDistortion:
recoError[counterError] = ISDistortion(SX, hatSX)
if verbose:
print("Reconstruction error difference after HPHI : ", \
recoError[counterError] - recoError[counterError - 1])
counterError += 1
# updating HM
tempNumFbyN = SX / np.maximum(hatSX ** 2, eps)
tempDenFbyN = 1 / np.maximum(hatSX, eps)
HM = np.maximum(HM * (np.dot(WM.T, tempNumFbyN) / \
np.maximum(np.dot(WM.T, tempDenFbyN), eps)) ** \
omega, eps)
SM = np.maximum(np.dot(WM, HM), eps)
hatSX = np.maximum(SF0 * SPHI + SM, eps)
if computeISDistortion:
recoError[counterError] = ISDistortion(SX, hatSX)
if verbose:
print("Reconstruction error difference after HM : ", \
recoError[counterError] - recoError[counterError - 1])
counterError += 1
# updating HGAMMA
if updateHGAMMA:
tempNumFbyN = (SF0 * SX) / np.maximum(hatSX ** 2, eps)
tempDenFbyN = SF0 / np.maximum(hatSX, eps)
HGAMMA = np.maximum(\
HGAMMA * (np.dot(WGAMMA.T, \
np.dot(tempNumFbyN, HPHI.T)) / \
np.maximum(\
np.dot(WGAMMA.T, \
np.dot(tempDenFbyN, HPHI.T)),
eps)) ** \
omega, eps)
sumHGAMMA = np.sum(HGAMMA, axis=0)
HGAMMA[:, sumHGAMMA>0] = HGAMMA[:, sumHGAMMA>0] / \
np.outer(np.ones(P), \
sumHGAMMA[sumHGAMMA>0])
HPHI = HPHI * np.outer(sumHGAMMA, np.ones(N))
sumHPHI = np.sum(HPHI, axis=0)
HPHI[:, sumHPHI>0] = HPHI[:, sumHPHI>0] / np.outer(np.ones(K), sumHPHI[sumHPHI>0])
HF0 = HF0 * np.outer(np.ones(NF0), sumHPHI)
WPHI = np.maximum(np.dot(WGAMMA, HGAMMA), eps)
SF0 = np.maximum(np.dot(WF0, HF0), eps)
SPHI = np.maximum(np.dot(WPHI, HPHI), eps)
hatSX = np.maximum(SF0 * SPHI + SM, eps)
if computeISDistortion:
recoError[counterError] = ISDistortion(SX, hatSX)
if verbose:
print("Reconstruction error difference after HGAMMA: ",)
print(recoError[counterError] - recoError[counterError - 1])
counterError += 1
# updating WM, after a certain number of iterations (here, after 1 iteration)
if n > -1: # this test can be used such that WM is updated only
# after a certain number of iterations
tempNumFbyN = SX / np.maximum(hatSX ** 2, eps)
tempDenFbyN = 1 / np.maximum(hatSX, eps)
WM = np.maximum(WM * (np.dot(tempNumFbyN, HM.T) /
np.maximum(np.dot(tempDenFbyN, HM.T),
eps)) ** omega, eps)
sumWM = np.sum(WM, axis=0)
WM[:, sumWM>0] = (WM[:, sumWM>0] /
np.outer(np.ones(F),sumWM[sumWM>0]))
HM = HM * np.outer(sumWM, np.ones(N))
SM = np.maximum(np.dot(WM, HM), eps)
hatSX = np.maximum(SF0 * SPHI + SM, eps)
if computeISDistortion:
recoError[counterError] = ISDistortion(SX, hatSX)
if verbose:
print("Reconstruction error difference after WM : ",)
print(recoError[counterError] - recoError[counterError - 1])
counterError += 1
return HGAMMA, HPHI, HF0, HM, WM, recoError
def Stereo_SIMM(# the data to be fitted to:
SXR, SXL,
# the basis matrices for the spectral combs
WF0,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=4, numberOfAccompanimentSpectralShapes=10,
# if any, initial amplitude matrices for
HGAMMA0=None, HPHI0=None,
HF00=None,
WM0=None, HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=1000, updateRulePower=1.0,
stepNotes=4,
lambdaHF0=0.00,alphaHF0=0.99,
displayEvolution=False, verbose=True,
updateHGAMMA=True):
"""
HGAMMA, HPHI, HF0, HM, WM, recoError =
SIMM(SXR, SXL, WF0, WGAMMA, numberOfFilters=4,
numberOfAccompanimentSpectralShapes=10, HGAMMA0=None, HPHI0=None,
HF00=None, WM0=None, HM0=None, numberOfIterations=1000,
updateRulePower=1.0, stepNotes=4,
lambdaHF0=0.00, alphaHF0=0.99, displayEvolution=False,
verbose=True)
Implementation of the Smooth-filters Instantaneous Mixture Model
(SIMM). This model can be used to estimate the main melody of a
song, and separate the lead voice from the accompaniment, provided
that the basis WF0 is constituted of elements associated to
particular pitches.
Inputs:
SX
the F x N power spectrogram to be approximated.
F is the number of frequency bins, while N is the number of
analysis frames
WF0
the F x NF0 basis matrix containing the NF0 source elements
WGAMMA
the F x P basis matrix of P smooth elementary filters
numberOfFilters
the number of filters K to be considered
numberOfAccompanimentSpectralShapes
the number of spectral shapes R for the accompaniment
HGAMMA0
the P x K decomposition matrix of WPHI on WGAMMA
HPHI0
the K x N amplitude matrix of the filter part of the lead
instrument
HF00
the NF0 x N amplitude matrix for the source part of the lead
instrument
WM0
the F x R the matrix for spectral shapes of the
accompaniment
HM0
the R x N amplitude matrix associated with each of the R
accompaniment spectral shapes
numberOfIterations
the number of iterations for the estimatino algorithm
updateRulePower
the power to which the multiplicative gradient is elevated to
stepNotes
the number of elements in WF0 per semitone. stepNotes=4 means
that there are 48 elements per octave in WF0.
lambdaHF0
Lagrangian multiplier for the octave control
alphaHF0
parameter that controls how much influence a lower octave
can have on the upper octave's amplitude.
Outputs:
HGAMMA
the estimated P x K decomposition matrix of WPHI on WGAMMA
HPHI
the estimated K x N amplitude matrix of the filter part
HF0
the estimated NF0 x N amplitude matrix for the source part
HM
the estimated R x N amplitude matrix for the accompaniment
WM
the estimate F x R spectral shapes for the accompaniment
recoError
the successive values of the Itakura Saito divergence
between the power spectrogram and the spectrogram
computed thanks to the updated estimations of the matrices.
Please also refer to the following article for more details about
the algorithm within this function, as well as the meaning of the
different matrices that are involved:
J.-L. Durrieu, G. Richard, B. David and C. Fevotte
Source/Filter Model for Unsupervised Main Melody
Extraction From Polyphonic Audio Signals
IEEE Transactions on Audio, Speech and Language Processing
Vol. 18, No. 3, March 2010
"""
eps = 10 ** (-20)
if displayEvolution:
import matplotlib.pyplot as plt
from imageMatlab import imageM
plt.ion()
print "Is the display interactive? ", plt.isinteractive()
# renamed for convenience:
K = numberOfFilters
R = numberOfAccompanimentSpectralShapes
omega = updateRulePower
F, N = SXR.shape
if (F, N) != SXL.shape:
print "The input STFT matrices do not have the same dimension.\n"
print "Please check what happened..."
raise ValueError("Dimension of STFT matrices must be the same.")
Fwf0, NF0 = WF0.shape
Fwgamma, P = WGAMMA.shape
# Checking the sizes of the matrices
if Fwf0 != F:
return False # A REVOIR!!!
if HGAMMA0 is None:
HGAMMA0 = np.abs(randn(P, K))
else:
if not(isinstance(HGAMMA0,np.ndarray)): # default behaviour
HGAMMA0 = np.array(HGAMMA0)
Phgamma0, Khgamma0 = HGAMMA0.shape
if Phgamma0 != P or Khgamma0 != K:
print "Wrong dimensions for given HGAMMA0, \n"
print "random initialization used instead"
HGAMMA0 = np.abs(randn(P, K))
HGAMMA = np.copy(HGAMMA0)
if HPHI0 is None: # default behaviour
HPHI = np.abs(randn(K, N))
else:
Khphi0, Nhphi0 = np.array(HPHI0).shape
if Khphi0 != K or Nhphi0 != N:
print "Wrong dimensions for given HPHI0, \n"
print "random initialization used instead"
HPHI = np.abs(randn(K, N))
else:
HPHI = np.copy(np.array(HPHI0))
if HF00 is None:
HF00 = np.abs(randn(NF0, N))
else:
if np.array(HF00).shape[0] == NF0 and np.array(HF00).shape[1] == N:
HF00 = np.array(HF00)
else:
print "Wrong dimensions for given HF00, \n"
print "random initialization used instead"
HF00 = np.abs(randn(NF0, N))
HF0 = np.copy(HF00)
if HM0 is None:
HM0 = np.abs(randn(R, N))
else:
if np.array(HM0).shape[0] == R and np.array(HM0).shape[1] == N:
HM0 = np.array(HM0)
else:
print "Wrong dimensions for given HM0, \n"
print "random initialization used instead"
HM0 = np.abs(randn(R, N))
HM = np.copy(HM0)
if WM0 is None:
WM0 = np.abs(randn(F, R))
else:
if np.array(WM0).shape[0] == F and np.array(WM0).shape[1] == R:
WM0 = np.array(WM0)
else:
print "Wrong dimensions for given WM0, \n"
print "random initialization used instead"
WM0 = np.abs(randn(F, R))
WM = np.copy(WM0)
alphaR = 0.5
alphaL = 0.5
betaR = np.diag(np.random.rand(R))
betaL = np.eye(R) - betaR
# Iterations to estimate the SIMM parameters:
WPHI = np.dot(WGAMMA, HGAMMA)
SF0 = np.dot(WF0, HF0)
SPHI = np.dot(WPHI, HPHI)
# SM = np.dot(WM, HM)
hatSXR = (alphaR**2) * SF0 * SPHI + np.dot(np.dot(WM, betaR**2),HM)
hatSXL = (alphaL**2) * SF0 * SPHI + np.dot(np.dot(WM, betaL**2),HM)
# SX = SX + np.abs(randn(F, N)) ** 2
# should not need this line
# which ensures that data is not
# 0 everywhere.
# temporary matrices
tempNumFbyN = np.zeros([F, N])
tempDenFbyN = np.zeros([F, N])
# Array containing the reconstruction error after the update of each
# of the parameter matrices:
recoError = np.zeros([numberOfIterations * 5 * 2 + NF0 * 2 + 1])
recoError[0] = ISDistortion(SXR, hatSXR) + ISDistortion(SXL, hatSXL)
if verbose:
print "Reconstruction error at beginning: ", recoError[0]
counterError = 1
if displayEvolution:
h1 = plt.figure(1)
# Main loop for multiplicative updating rules:
for n in np.arange(numberOfIterations):
# order of re-estimation: HF0, HPHI, HM, HGAMMA, WM
if verbose:
print "iteration ", n, " over ", numberOfIterations
if displayEvolution:
h1.clf();imageM(db(HF0));
plt.clim([np.amax(db(HF0))-100, np.amax(db(HF0))]);plt.draw();
# h1.clf();
# imageM(HF0 * np.outer(np.ones([NF0, 1]),
# 1 / (HF0.max(axis=0))));
# updating HF0:
tempNumFbyN = ((alphaR**2) * SPHI * SXR) / np.maximum(hatSXR ** 2, eps)\
+ ((alphaL**2) * SPHI * SXL) / np.maximum(hatSXL ** 2, eps)
tempDenFbyN = (alphaR**2) * SPHI / np.maximum(hatSXR, eps)\
+ (alphaL**2) * SPHI / np.maximum(hatSXL, eps)
# This to enable octave control
HF0[np.arange(12 * stepNotes, NF0), :] \
= HF0[np.arange(12 * stepNotes, NF0), :] \
* (np.dot(WF0[:, np.arange(12 * stepNotes,
NF0)].T, tempNumFbyN) \
/ np.maximum(
np.dot(WF0[:, np.arange(12 * stepNotes, NF0)].T,
tempDenFbyN) \
+ lambdaHF0 * (- (alphaHF0 - 1.0) \
/ np.maximum(HF0[
np.arange(12 * stepNotes, NF0), :], eps) \
+ HF0[
np.arange(NF0 - 12 * stepNotes), :]),
eps)) ** omega
HF0[np.arange(12 * stepNotes), :] \
= HF0[np.arange(12 * stepNotes), :] \
* (np.dot(WF0[:, np.arange(12 * stepNotes)].T,
tempNumFbyN) /
np.maximum(
np.dot(WF0[:, np.arange(12 * stepNotes)].T,
tempDenFbyN), eps)) ** omega
## # normal update rules:
## HF0 = HF0 * (np.dot(WF0.T, tempNumFbyN) /
## np.maximum(np.dot(WF0.T, tempDenFbyN), eps)) ** omega
SF0 = np.maximum(np.dot(WF0, HF0), eps)
hatSXR = np.maximum((alphaR**2) * SF0 * SPHI + \
np.dot(np.dot(WM, betaR**2),HM),
eps)
hatSXL = np.maximum((alphaL**2) * SF0 * SPHI + \
np.dot(np.dot(WM, betaL**2),HM),
eps)
## recoError[counterError] = ISDistortion(SXR, hatSXR) \
## + ISDistortion(SXL, hatSXL)
##
## if verbose:
## print "Reconstruction error difference after HF0 : ",
## print recoError[counterError] - recoError[counterError - 1]
counterError += 1
# updating HPHI
if updateHGAMMA or True:
tempNumFbyN = ((alphaR**2) * SF0 * SXR) / np.maximum(hatSXR ** 2, eps)\
+ ((alphaL**2) * SF0 * SXL) / np.maximum(hatSXL ** 2, eps)
tempDenFbyN = (alphaR**2) * SF0 / np.maximum(hatSXR, eps)\
+ (alphaL**2) * SF0 / np.maximum(hatSXL, eps)
HPHI = HPHI * (np.dot(WPHI.T, tempNumFbyN) / np.maximum(np.dot(WPHI.T, tempDenFbyN), eps)) ** omega
sumHPHI = np.sum(HPHI, axis=0)
HPHI[:, sumHPHI>0] = HPHI[:, sumHPHI>0] / np.outer(np.ones(K), sumHPHI[sumHPHI>0])
HF0 = HF0 * np.outer(np.ones(NF0), sumHPHI)
SF0 = np.maximum(np.dot(WF0, HF0), eps)
SPHI = np.maximum(np.dot(WPHI, HPHI), eps)
hatSXR = np.maximum((alphaR**2) * SF0 * SPHI + \
np.dot(np.dot(WM, betaR**2),HM),
eps)
hatSXL = np.maximum((alphaL**2) * SF0 * SPHI + \
np.dot(np.dot(WM, betaL**2),HM),
eps)
## recoError[counterError] = ISDistortion(SXR, hatSXR) \
## + ISDistortion(SXL, hatSXL)
##
## if verbose:
## print "Reconstruction error difference after HPHI : ", recoError[counterError] - recoError[counterError - 1]
##
counterError += 1
# updating HM
# tempNumFbyN = SXR / np.maximum(hatSXR ** 2, eps)\
# + SXL / np.maximum(hatSXL ** 2, eps)
# tempDenFbyN = 1 / np.maximum(hatSXR, eps)\
# + 1 / np.maximum(hatSXL, eps)
# HM = np.maximum(HM * (np.dot(WM.T, tempNumFbyN) / np.maximum(np.dot(WM.T, tempDenFbyN), eps)) ** omega, eps)
HM = HM * \
((np.dot(np.dot((betaR**2), WM.T), SXR /
np.maximum(hatSXR ** 2, eps)) +
np.dot(np.dot((betaL**2), WM.T), SXL /
np.maximum(hatSXL ** 2, eps))
) /
np.maximum(np.dot(np.dot((betaR**2), WM.T), 1 /
np.maximum(hatSXR, eps)) +
np.dot(np.dot((betaL**2), WM.T), 1 /
np.maximum(hatSXL, eps)),
eps)) ** omega
hatSXR = np.maximum((alphaR**2) * SF0 * SPHI + \
np.dot(np.dot(WM, betaR**2),HM), eps)
hatSXL = np.maximum((alphaL**2) * SF0 * SPHI + \
np.dot(np.dot(WM, betaL**2),HM), eps)
## recoError[counterError] = ISDistortion(SXR, hatSXR) \
## + ISDistortion(SXL, hatSXL)
##
## if verbose:
## print "Reconstruction error difference after HM : ", recoError[counterError] - recoError[counterError - 1]
counterError += 1
# updating HGAMMA
if updateHGAMMA:
tempNumFbyN = ((alphaR ** 2) * SF0 * SXR) / np.maximum(hatSXR ** 2, eps)\
+ ((alphaL ** 2) * SF0 * SXL) / np.maximum(hatSXL ** 2, eps)
tempDenFbyN = (alphaR ** 2) * SF0 / np.maximum(hatSXR, eps) \
+ (alphaL ** 2) * SF0 / np.maximum(hatSXL, eps)
HGAMMA = np.maximum(HGAMMA * (np.dot(WGAMMA.T, np.dot(tempNumFbyN, HPHI.T)) / np.maximum(np.dot(WGAMMA.T, np.dot(tempDenFbyN, HPHI.T)), eps)) ** omega, eps)
sumHGAMMA = np.sum(HGAMMA, axis=0)
HGAMMA[:, sumHGAMMA>0] = HGAMMA[:, sumHGAMMA>0] / np.outer(np.ones(P), sumHGAMMA[sumHGAMMA>0])
HPHI = HPHI * np.outer(sumHGAMMA, np.ones(N))
sumHPHI = np.sum(HPHI, axis=0)
HPHI[:, sumHPHI>0] = HPHI[:, sumHPHI>0] / np.outer(np.ones(K), sumHPHI[sumHPHI>0])
HF0 = HF0 * np.outer(np.ones(NF0), sumHPHI)
WPHI = np.maximum(np.dot(WGAMMA, HGAMMA), eps)
SF0 = np.maximum(np.dot(WF0, HF0), eps)
SPHI = np.maximum(np.dot(WPHI, HPHI), eps)
hatSXR = np.maximum((alphaR**2) * SF0 * SPHI + \
np.dot(np.dot(WM, betaR**2),HM), eps)
hatSXL = np.maximum((alphaL**2) * SF0 * SPHI + \
np.dot(np.dot(WM, betaL**2),HM), eps)
## recoError[counterError] = ISDistortion(SXR, hatSXR) \
## + ISDistortion(SXL, hatSXL)
##
## if verbose:
## print "Reconstruction error difference after HGAMMA: ",
## print recoError[counterError] - recoError[counterError - 1]
##
counterError += 1
# updating WM, after a certain number of iterations (here, after 1 iteration)
if n > -1: # this test can be used such that WM is updated only
# after a certain number of iterations
## tempNumFbyN = SX / np.maximum(hatSX ** 2, eps)
## tempDenFbyN = 1 / np.maximum(hatSX, eps)
## WM = np.maximum(WM * (np.dot(tempNumFbyN, HM.T) /
## np.maximum(np.dot(tempDenFbyN, HM.T),
## eps)) ** omega, eps)
WM = WM * \
((np.dot(SXR / np.maximum(hatSXR ** 2, eps),
np.dot(HM.T, betaR ** 2)) +
np.dot(SXL / np.maximum(hatSXL ** 2, eps),
np.dot(HM.T, betaL ** 2))
) /
(np.dot(1 / np.maximum(hatSXR, eps),
np.dot(HM.T, betaR ** 2)) +
np.dot(1 / np.maximum(hatSXL, eps),
np.dot(HM.T, betaL ** 2))
)) ** omega
sumWM = np.sum(WM, axis=0)
WM[:, sumWM>0] = (WM[:, sumWM>0] /
np.outer(np.ones(F),sumWM[sumWM>0]))
HM = HM * np.outer(sumWM, np.ones(N))
hatSXR = np.maximum((alphaR**2) * SF0 * SPHI + \
np.dot(np.dot(WM, betaR**2),HM), eps)
hatSXL = np.maximum((alphaL**2) * SF0 * SPHI + \
np.dot(np.dot(WM, betaL**2),HM), eps)
## recoError[counterError] = ISDistortion(SXR, hatSXR) \
## + ISDistortion(SXL, hatSXL)
##
## if verbose:
## print "Reconstruction error difference after WM : ",
## print recoError[counterError] - recoError[counterError - 1]
counterError += 1
# updating alphaR and alphaL:
tempNumFbyN = SF0 * SPHI * SXR / np.maximum(hatSXR ** 2, eps)
tempDenFbyN = SF0 * SPHI / np.maximum(hatSXR, eps)
alphaR = np.maximum(alphaR *
(np.sum(tempNumFbyN) /
np.sum(tempDenFbyN)) ** (omega*.1), eps)
tempNumFbyN = SF0 * SPHI * SXL / np.maximum(hatSXL ** 2, eps)
tempDenFbyN = SF0 * SPHI / np.maximum(hatSXL, eps)
alphaL = np.maximum(alphaL *
(np.sum(tempNumFbyN) /
np.sum(tempDenFbyN)) ** (omega*.1), eps)
alphaR = alphaR / np.maximum(alphaR + alphaL, .001)
alphaL = np.copy(1 - alphaR)
hatSXR = np.maximum((alphaR**2) * SF0 * SPHI + \
np.dot(np.dot(WM, betaR**2),HM), eps)
hatSXL = np.maximum((alphaL**2) * SF0 * SPHI + \
np.dot(np.dot(WM, betaL**2),HM), eps)
## recoError[counterError] = ISDistortion(SXR, hatSXR) \
## + ISDistortion(SXL, hatSXL)
##
## if verbose:
## print "Reconstruction error difference after ALPHA : ",
## print recoError[counterError] - recoError[counterError - 1]
counterError += 1
# updating betaR and betaL
betaR = np.diag(np.diag(np.maximum(betaR *
((np.dot(np.dot(WM.T, SXR / np.maximum(hatSXR ** 2, eps)), HM.T)) /
(np.dot(np.dot(WM.T, 1 / np.maximum(hatSXR, eps)), HM.T))) ** (omega*.1), eps)))
betaL = np.diag(np.diag(np.maximum(betaL *
((np.dot(np.dot(WM.T, SXL / np.maximum(hatSXL ** 2, eps)), HM.T)) /
(np.dot(np.dot(WM.T, 1 / np.maximum(hatSXL, eps)), HM.T))) ** (omega*.1), eps)))
betaR = betaR / np.maximum(betaR + betaL, eps)
betaL = np.copy(np.eye(R) - betaR)
hatSXR = np.maximum((alphaR**2) * SF0 * SPHI + \
np.dot(np.dot(WM, betaR**2),HM), eps)
hatSXL = np.maximum((alphaL**2) * SF0 * SPHI + \
np.dot(np.dot(WM, betaL**2),HM), eps)
## recoError[counterError] = ISDistortion(SXR, hatSXR) \
## + ISDistortion(SXL, hatSXL)
##
## if verbose:
## print "Reconstruction error difference after BETA : ",
## print recoError[counterError] - recoError[counterError - 1]
counterError += 1
return alphaR, alphaL, HGAMMA, HPHI, HF0, betaR, betaL, HM, WM, recoError
def main(inputAudioFile):
import optparse
usage = "usage: %prog [options] inputAudioFile"
parser = optparse.OptionParser(usage)
# Name of the output files:
parser.add_option("-v", "--vocal-output-file",
dest="voc_output_file", type="string",
help="name of the audio output file for the estimated\n"\
"solo (vocal) part",
default="estimated_solo.wav")
parser.add_option("-m", "--music-output-file",
dest="mus_output_file", type="string",
help="name of the audio output file for the estimated\n"\
"music part",
default="estimated_music.wav")
parser.add_option("-p",
"--pitch-output-file",
dest="pitch_output_file",
type="string",
help="name of the output file for the estimated pitches",
default="pitches.txt")
# Some more optional options:
parser.add_option("-d",
"--with-display",
dest="displayEvolution",
action="store_true",
help="display the figures",
default=False)
parser.add_option("-q",
"--quiet",
dest="verbose",
action="store_false",
help="use to quiet all output verbose",
default=True)
#Number of iterations
parser.add_option("--nb-iterations",
dest="nbiter",
help="number of iterations",
type="int",
default=50)
parser.add_option("--window-size",
dest="windowSize",
type="float",
default=0.04644,
help="size of analysis windows, in s.")
parser.add_option("--Fourier-size", dest="fourierSize", type="int",
default=2048,
help="size of Fourier transforms, "\
"in samples.")
parser.add_option("--hopsize",
dest="hopsize",
type="float",
default=0.0058,
help="size of the hop between analysis windows, in s.")
parser.add_option("--nb-accElements",
dest="R",
type="float",
default=40.0,
help="number of elements for the accompaniment.")
parser.add_option("--with-melody", dest="melody", type="string",
default=None,
help="provide the melody in a file named MELODY, "\
"with at each line: <time (s)><F0 (Hz)>.")
(options, args) = parser.parse_args()
#if len(args) != 1:
#parser.error("incorrect number of arguments, use option -h for help.")
displayEvolution = options.displayEvolution
if displayEvolution:
import matplotlib.pyplot as plt
import imageMatlab
## plt.rc('text', usetex=True)
plt.rc('image', cmap='jet') ## gray_r
plt.ion()
# Compulsory option: name of the input file:
#inputAudioFile = args[0]
fs, data = wav.read(inputAudioFile)
# data = np.double(data) / 32768.0 # makes data vary from -1 to 1
scaleData = 1.2 * data.max() # to rescale the data.
dataType = data.dtype
data = np.double(data) / scaleData # makes data vary from -1 to 1
if data.shape[0] == data.size: # data is multi-channel
print("The audio file is not stereo. Try separateLead.py instead.")
raise ValueError("number of dimensions of the input not 2")
if data.shape[1] != 2:
print("The data is multichannel, but not stereo... \n")
print("Unfortunately this program does not scale well. Data is \n")
print("reduced to its 2 first channels.\n")
data = data[:, 0:2]
# Processing the options:
windowSizeInSamples = np.round(options.windowSize * fs)
hopsize = np.round(options.hopsize * fs)
NFT = options.fourierSize
niter = options.nbiter
R = options.R
if options.verbose:
print("Some parameter settings:")
print(" Size of analysis windows: ", windowSizeInSamples)
print(" Hopsize: ", hopsize)
print(" Size of Fourier transforms: ", NFT)
print(" Number of iterations to be done: ", niter)
print(" Number of elements in WM: ", R)
XR, F, N = stft(data[:, 0],
fs=fs,
hopsize=hopsize,
window=sinebell(windowSizeInSamples),
nfft=NFT)
XL, F, N = stft(data[:, 1],
fs=fs,
hopsize=hopsize,
window=sinebell(windowSizeInSamples),
nfft=NFT)
# SX is the power spectrogram:
## SXR = np.maximum(np.abs(XR) ** 2, 10 ** -8)
## SXL = np.maximum(np.abs(XL) ** 2, 10 ** -8)
SXR = np.abs(XR)**2
SXL = np.abs(XL)**2
del data, F, N
# TODO: also process these as options:
eps = 10**-9
minF0 = 100
maxF0 = 800
Fs = fs
F, N = SXR.shape
stepNotes = 20 # this is the number of F0s within one semitone
# until 17/09/2010 : stepNotes = 20
# 17/09/2010 : trying stepNotes = 8, checking for less artefacts
K = 10 # number of spectral shapes for the filter part
# R = 40 # number of spectral shapes for the accompaniment
P = 30 # number of elements in dictionary of smooth filters
chirpPerF0 = 1 # number of chirped spectral shapes between each F0
# this feature should be further studied before
# we find a good way of doing that.
# Create the harmonic combs, for each F0 between minF0 and maxF0:
F0Table, WF0 = \
generate_WF0_chirped(minF0, maxF0, Fs, Nfft=NFT, \
stepNotes=stepNotes, \
lengthWindow=windowSizeInSamples, Ot=0.25, \
perF0=chirpPerF0, \
depthChirpInSemiTone=.15, loadWF0=True,\
analysisWindow='sinebell')
WF0 = WF0[0:F, :] # ensure same size as SX
NF0 = F0Table.size # number of harmonic combs
# Normalization:
WF0 = WF0 / np.outer(np.ones(F), np.amax(WF0, axis=0))
# Create the dictionary of smooth filters, for the filter part of
# the lead isntrument:
WGAMMA = generateHannBasis(F, NFT, Fs=fs, frequencyScale='linear', \
numberOfBasis=P, overlap=.75)
if displayEvolution:
plt.figure(1)
plt.clf()
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel(r'Frame number $n$', fontsize=16)
plt.ylabel(r'Leading source number $u$', fontsize=16)
plt.ion()
# plt.show()
## the following seems superfluous if mpl's backend is macosx...
## raw_input("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"\
## "!! Press Return to resume the program. !!\n"\
## "!! Be sure that the figure has been !!\n"\
## "!! already displayed, so that the !!\n"\
## "!! evolution of HF0 will be visible. !!\n"\
## "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
if options.melody is None:
## section to estimate the melody, on monophonic algo:
SX = np.maximum(np.abs((XR + XL) / 2.0)**2, 10**-8)
# First round of parameter estimation:
HGAMMA, HPHI, HF0, HM, WM, recoError1 = SIMM.SIMM(
# the data to be fitted to:
SX,
# the basis matrices for the spectral combs
WF0,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K,
numberOfAccompanimentSpectralShapes=R,
# putting only 2 elements in accompaniment for a start...
# if any, initial amplitude matrices for
HGAMMA0=None,
HPHI0=None,
HF00=None,
WM0=None,
HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter,
updateRulePower=1.,
stepNotes=stepNotes,
lambdaHF0=0.0 / (1.0 * SX.max()),
alphaHF0=0.9,
verbose=options.verbose,
displayEvolution=displayEvolution)
if displayEvolution:
h2 = plt.figure(2)
plt.clf()
imageMatlab.imageM(20 * np.log10(HF0))
matMax = (20 * np.log10(HF0)).max()
matMed = np.median(20 * np.log10(HF0))
plt.clim([matMed - 100, matMax])
# Viterbi decoding to estimate the predominant fundamental
# frequency line
scale = 1.0
transitions = np.exp(-np.floor(np.arange(0, NF0) / stepNotes) * scale)
cutoffnote = 2 * 5 * stepNotes
transitions[cutoffnote:] = transitions[cutoffnote - 1]
transitionMatrixF0 = np.zeros([NF0 + 1, NF0 + 1]) # toeplitz matrix
b = np.arange(NF0)
transitionMatrixF0[0:NF0, 0:NF0] = \
transitions[\
np.array(np.abs(np.outer(np.ones(NF0), b) \
- np.outer(b, np.ones(NF0))), dtype=int)]
pf_0 = transitions[cutoffnote - 1] * 10**(-90)
p0_0 = transitions[cutoffnote - 1] * 10**(-100)
p0_f = transitions[cutoffnote - 1] * 10**(-80)
transitionMatrixF0[0:NF0, NF0] = pf_0
transitionMatrixF0[NF0, 0:NF0] = p0_f
transitionMatrixF0[NF0, NF0] = p0_0
sumTransitionMatrixF0 = np.sum(transitionMatrixF0, axis=1)
transitionMatrixF0 = transitionMatrixF0 \
/ np.outer(sumTransitionMatrixF0, \
np.ones(NF0 + 1))
priorProbabilities = 1 / (NF0 + 1.0) * np.ones([NF0 + 1])
logHF0 = np.zeros([NF0 + 1, N])
normHF0 = np.amax(HF0, axis=0)
barHF0 = np.array(HF0)
logHF0[0:NF0, :] = np.log(barHF0)
logHF0[0:NF0, normHF0 == 0] = np.amin(logHF0[logHF0 > -np.Inf])
logHF0[NF0, :] = np.maximum(np.amin(logHF0[logHF0 > -np.Inf]), -100)
indexBestPath = viterbiTrackingArray(\
logHF0, np.log(priorProbabilities),
np.log(transitionMatrixF0), verbose=options.verbose)
if displayEvolution:
h2.hold(True)
plt.plot(indexBestPath, '-b')
h2.hold(False)
plt.axis('tight')
## raw_input("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"\
## "!! Press Return to resume the program !!\n"\
## "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
del logHF0
# detection of silences:
HF00 = np.zeros([NF0 * chirpPerF0, N])
scopeAllowedHF0 = 2.0 / 1.0
dim1index = np.array(\
np.maximum(\
np.minimum(\
np.outer(chirpPerF0 * indexBestPath,
np.ones(chirpPerF0 \
* (2 \
* np.floor(stepNotes / scopeAllowedHF0) \
+ 1))) \
+ np.outer(np.ones(N),
np.arange(-chirpPerF0 \
* np.floor(stepNotes / scopeAllowedHF0),
chirpPerF0 \
* (np.floor(stepNotes / scopeAllowedHF0) \
+ 1))),
chirpPerF0 * NF0 - 1),
0),
dtype=int).reshape(1, N * chirpPerF0 \
* (2 * np.floor(stepNotes / scopeAllowedHF0) \
+ 1))
dim2index = np.outer(np.arange(N),
np.ones(chirpPerF0 \
* (2 * np.floor(stepNotes \
/ scopeAllowedHF0) + 1), \
dtype=int)\
).reshape(1, N * chirpPerF0 \
* (2 * np.floor(stepNotes \
/ scopeAllowedHF0) \
+ 1))
HF00[dim1index, dim2index] = HF0[dim1index, dim2index] # HF0.max()
HF00[:, indexBestPath == (NF0 - 1)] = 0.0
HF00[:, indexBestPath == 0] = 0.0
thres_energy = 0.000584
SF0 = np.maximum(np.dot(WF0, HF00), eps)
SPHI = np.maximum(np.dot(WGAMMA, np.dot(HGAMMA, HPHI)), eps)
SM = np.maximum(np.dot(WM, HM), eps)
hatSX = np.maximum(SPHI * SF0 + SM, eps)
energyMel = np.sum(np.abs((SPHI * SF0)/hatSX * \
(XR+XL) * 0.5) \
** 2, axis=0)
energyMelSorted = np.sort(energyMel)
energyMelCumul = np.cumsum(energyMelSorted)
energyMelCumulNorm = energyMelCumul / max(energyMelCumul[-1], eps)
# normalized to the maximum of energy:
# expressed in 0.01 times the percentage
ind_999 = np.nonzero(energyMelCumulNorm > thres_energy)[0][0]
if ind_999 is None:
ind_999 = N
melNotPresent = (energyMel <= energyMelCumulNorm[ind_999])
indexBestPath[melNotPresent] = 0
else:
## take the provided melody line:
# load melody from file:
melodyFromFile = np.loadtxt(options.melody)
sizeProvidedMel = melodyFromFile.shape
if len(sizeProvidedMel) == 1:
print("The melody should be provided as <Time (s)><F0 (Hz)>.")
raise ValueError("Bad melody format")
melTimeStamps = melodyFromFile[:, 0] # + 1024 / np.double(Fs)
melFreqHz = melodyFromFile[:, 1]
if minF0 > melFreqHz[
melFreqHz > 40.0].min() or maxF0 < melFreqHz.max():
minF0 = melFreqHz[melFreqHz > 40.0].min() * .97
maxF0 = np.maximum(melFreqHz.max() * 1.03, 2 * minF0 * 1.03)
print("Recomputing the source basis for ")
print("minF0 = ", minF0, "Hz and maxF0 = ", maxF0, "Hz.")
# Create the harmonic combs, for each F0 between minF0 and maxF0:
F0Table, WF0 = \
generate_WF0_chirped(minF0, maxF0, Fs, Nfft=NFT, \
stepNotes=stepNotes, \
lengthWindow=windowSizeInSamples,
Ot=0.25, \
perF0=chirpPerF0, \
depthChirpInSemiTone=.15)
WF0 = WF0[0:F, :] # ensure same size as SX
NF0 = F0Table.size # number of harmonic combs
# Normalization:
WF0 = WF0 / np.outer(np.ones(F), np.amax(WF0, axis=0))
sigTimeStamps = np.arange(N) * hopsize / np.double(Fs)
distMatTimeStamps = np.abs(
np.outer(np.ones(sizeProvidedMel[0]), sigTimeStamps) -
np.outer(melTimeStamps, np.ones(N)))
minDistTimeStamps = distMatTimeStamps.argmin(axis=0)
f0BestPath = melFreqHz[minDistTimeStamps]
distMatF0 = np.abs(
np.outer(np.ones(NF0), f0BestPath) - np.outer(F0Table, np.ones(N)))
indexBestPath = distMatF0.argmin(axis=0)
# setting silences to 0, with tolerance = 1/2 window length
indexBestPath[distMatTimeStamps[minDistTimeStamps,range(N)] >= \
0.5 * options.windowSize] = 0
indexBestPath[f0BestPath <= 0] = 0
freqMelody = F0Table[np.array(indexBestPath, dtype=int)]
freqMelody[indexBestPath == 0] = -freqMelody[indexBestPath == 0]
np.savetxt(
options.pitch_output_file,
np.array([np.arange(N) * hopsize / np.double(Fs), freqMelody]).T)
# Second round of parameter estimation, with specific
# initial HF00:
HF00 = np.zeros([NF0 * chirpPerF0, N])
scopeAllowedHF0 = 2.0 / 1.0
# indexes for HF00:
# TODO: reprogram this with a 'where'?...
dim1index = np.array(\
np.maximum(\
np.minimum(\
np.outer(chirpPerF0 * indexBestPath,
np.ones(chirpPerF0 \
* (2 \
* np.floor(stepNotes / scopeAllowedHF0) \
+ 1))) \
+ np.outer(np.ones(N),
np.arange(-chirpPerF0 \
* np.floor(stepNotes / scopeAllowedHF0),
chirpPerF0 \
* (np.floor(stepNotes / scopeAllowedHF0) \
+ 1))),
chirpPerF0 * NF0 - 1),
0),
dtype=int)
dim1index = dim1index[indexBestPath != 0, :]
## dim1index = dim1index.reshape(1, N * chirpPerF0 \
## * (2 * np.floor(stepNotes / scopeAllowedHF0) \
## + 1))
dim1index = dim1index.reshape(1, dim1index.size)
dim2index = np.outer(np.arange(N),
np.ones(chirpPerF0 \
* (2 * np.floor(stepNotes \
/ scopeAllowedHF0) + 1), \
dtype=int)\
)
dim2index = dim2index[indexBestPath != 0, :]
dim2index = dim2index.reshape(1, dim2index.size)
## dim2index.reshape(1, N * chirpPerF0 \
## * (2 * np.floor(stepNotes \
## / scopeAllowedHF0) \
## + 1))
HF00[dim1index, dim2index] = 1 # HF0.max()
HF00[:, indexBestPath == (NF0 - 1)] = 0.0
HF00[:, indexBestPath == 0] = 0.0
WF0effective = WF0
HF00effective = HF00
if options.melody is None:
del HF0, HGAMMA, HPHI, HM, WM, HF00, SX
alphaR, alphaL, HGAMMA, HPHI, HF0, \
betaR, betaL, HM, WM, recoError2 = SIMM.Stereo_SIMM(
# the data to be fitted to:
SXR, SXL,
# the basis matrices for the spectral combs
WF0effective,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K, numberOfAccompanimentSpectralShapes=R,
# if any, initial amplitude matrices for
HGAMMA0=None, HPHI0=None,
HF00=HF00effective,
WM0=None, HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter, updateRulePower=1.0,
stepNotes=stepNotes,
lambdaHF0 = 0.0 / (1.0 * SXR.max()), alphaHF0=0.9,
verbose=options.verbose, displayEvolution=displayEvolution)
WPHI = np.dot(WGAMMA, HGAMMA)
SPHI = np.dot(WPHI, HPHI)
SF0 = np.dot(WF0effective, HF0)
hatSXR = (alphaR**2) * SF0 * SPHI + np.dot(np.dot(WM, betaR**2), HM)
hatSXL = (alphaL**2) * SF0 * SPHI + np.dot(np.dot(WM, betaL**2), HM)
hatVR = (alphaR**2) * SPHI * SF0 / hatSXR * XR
vestR = istft(
hatVR, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
hatVR = (alphaL**2) * SPHI * SF0 / hatSXL * XL
vestL = istft(
hatVR, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
#scikits.audiolab.wavwrite(np.array([vestR,vestL]).T, \
# options.voc_output_file, fs)
vestR = np.array(np.round(vestR * scaleData), dtype=dataType)
vestL = np.array(np.round(vestL * scaleData), dtype=dataType)
# wav.write(options.voc_output_file, fs, \
# np.array([vestR,vestL]).T)
#wav.write(options.voc_output_file, fs, \
# np.int16(32768.0 * np.array([vestR,vestL]).T))
hatMR = (np.dot(np.dot(WM, betaR**2), HM)) / hatSXR * XR
mestR = istft(
hatMR, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
hatMR = (np.dot(np.dot(WM, betaL**2), HM)) / hatSXL * XL
mestL = istft(
hatMR, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
#scikits.audiolab.wavwrite(np.array([mestR,mestL]).T, \
# options.mus_output_file, fs)
mestR = np.array(np.round(mestR * scaleData), dtype=dataType)
mestL = np.array(np.round(mestL * scaleData), dtype=dataType)
# wav.write(options.mus_output_file, fs, \
# np.array([mestR,mestL]).T)
#wav.write(options.mus_output_file, fs, \
# np.int16(32768.0 * np.array([mestR,mestL]).T))
del hatMR, mestL, vestL, vestR, mestR, hatVR, hatSXR, hatSXL, SPHI, SF0
# adding the unvoiced part in the source basis:
WUF0 = np.hstack([WF0, np.ones([WF0.shape[0], 1])])
HUF0 = np.vstack([HF0, np.ones([1, HF0.shape[1]])])
## HUF0[-1,:] = HF0.sum(axis=0) # should we do this?
alphaR, alphaL, HGAMMA, HPHI, HF0, \
betaR, betaL, HM, WM, recoError3 = SIMM.Stereo_SIMM(
# the data to be fitted to:
SXR, SXL,
# the basis matrices for the spectral combs
WUF0,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K, numberOfAccompanimentSpectralShapes=R,
# if any, initial amplitude matrices for
HGAMMA0=HGAMMA, HPHI0=HPHI,
HF00=HUF0,
WM0=None,#WM,
HM0=None,#HM,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter, updateRulePower=1.0,
stepNotes=stepNotes,
lambdaHF0 = 0.0 / (1.0 * SXR.max()), alphaHF0=0.9,
verbose=options.verbose, displayEvolution=displayEvolution,
updateHGAMMA=False)
WPHI = np.dot(WGAMMA, HGAMMA)
SPHI = np.dot(WPHI, HPHI)
SF0 = np.dot(WUF0, HF0)
hatSXR = (alphaR**2) * SF0 * SPHI + np.dot(np.dot(WM, betaR**2), HM)
hatSXL = (alphaL**2) * SF0 * SPHI + np.dot(np.dot(WM, betaL**2), HM)
hatVR = (alphaR**2) * SPHI * SF0 / hatSXR * XR
vestR = istft(
hatVR, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
hatVR = (alphaL**2) * SPHI * SF0 / hatSXL * XL
vestL = istft(
hatVR, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
outputFileName = options.voc_output_file[:-4] + '_VUIMM.wav'
# scikits.audiolab.wavwrite(np.array([vestR,vestL]).T, outputFileName, fs)
vestR = np.array(np.round(vestR * scaleData), dtype=dataType)
vestL = np.array(np.round(vestL * scaleData), dtype=dataType)
# wav.write(outputFileName, fs, \
# np.array([vestR,vestL]).T)
hatMR = (np.dot(np.dot(WM, betaR**2), HM)) / hatSXR * XR
mestR = istft(
hatMR, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
hatMR = (np.dot(np.dot(WM, betaL**2), HM)) / hatSXL * XL
mestL = istft(
hatMR, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
#This is the required file
outputFileName = options.mus_output_file[:-4] + '_VUIMM.wav'
#scikits.audiolab.wavwrite(np.array([mestR,mestL]).T, outputFileName, fs)
os.chdir('media/karaoke')
mestR = np.array(np.round(mestR * scaleData), dtype=dataType)
mestL = np.array(np.round(mestL * scaleData), dtype=dataType)
wav.write(outputFileName, fs, \
np.array([mestR,mestL]).T)
if displayEvolution:
plt.close('all')
## raw_input("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"\
## "!! Press Return to end the program... !!\n"\
## "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
print("Done!")
print outputFileName
os.chdir('..')
os.chdir('..')
def main(args, options):
stereoEstimation = True
# Median filtering in spectrogram
HPS = False
displayEvolution = options.displayEvolution
if displayEvolution:
import matplotlib.pyplot as plt
import imageMatlab
## plt.rc('text', usetex=True)
plt.rc('image', cmap='jet') ## gray_r
plt.ion()
# Compulsory option: name of the input file:
inputAudioFile = ''
if len(args) >= 2:
inputAudioFile = args[0]
options.pitch_output_file = args[1]
if len(args) == 1:
inputAudioFile = args[0]
if len(args) == 0:
inputAudioFile = options.input_file
if inputAudioFile[-4:] != ".wav":
raise ValueError(
"File not WAV file? Only WAV format support, for now...")
#print "Writing the different following output files:"
if not (options.vit_pitch_output_file is None):
print " estimated pitches in", options.vit_pitch_output_file
if not (options.sal_output_file is None):
print " salience file in ", options.sal_output_file
if options.pitch_output_file is None:
options.pitch_output_file = inputAudioFile[:-4] + '_pitches.txt'
try:
from essentia.standard import AudioLoader
loaded = AudioLoader(filename=inputAudioFile)()
audio = loaded[0]
Fs = loaded[1]
nchan = loaded[2]
loaded = AudioLoader(filename=inputAudioFile)()
audio = loaded[0]
if nchan == 1:
data = audio[:, 0].transpose()
else:
data = audio.transpose()
data = np.double(data) / (1.2 * abs(data).max())
except:
# Using scipy to import wav
import scipy.io.wavfile as wav
Fs, data = wav.read(inputAudioFile)
# data = np.double(data) / 32768.0 # makes data vary from -1 to 1
scaleData = 1.2 * data.max() # to rescale the data.
data = np.double(data) / scaleData # makes data vary from -1 to 1
options.Fs = Fs
is_stereo = True
if data.shape[0] == data.size: # data is multi-channel
#print "The audio file is not stereo."
#print "The audio file is not stereo. Making stereo out of mono."
#print "(You could also try the older separateLead.py...)"
is_stereo = False
# data = np.vstack([data,data]).T
# raise ValueError("number of dimensions of the input not 2")
if is_stereo and data.shape[1] != 2:
print "The data is multichannel, but not stereo... \n"
print "Unfortunately this program does not scale well. Data is \n"
print "reduced to its 2 first channels.\n"
data = data[:, 0:2]
# Processing the options:
windowSizeInSamples = nextpow2(np.round(options.windowSize * Fs))
hopsize = np.round(options.hopsize * Fs)
#if hopsize != windowSizeInSamples/8:
# #print "Overriding given hopsize to use 1/8th of window size"
# #hopsize = windowSizeInSamples/8
# warnings.warn("Chosen hopsize: "+str(hopsize)+\
# ", while windowsize: "+str(windowSizeInSamples))
options.hopsizeInSamples = hopsize
if options.fourierSize is None:
NFT = windowSizeInSamples
else:
NFT = options.fourierSize
# number of iterations for each parameter estimation step:
niter = options.nbiter
# number of spectral shapes for the accompaniment
R = int(options.R)
eps = 10**-9
if options.verbose:
print "Some parameter settings:"
print " Size of analysis windows: ", windowSizeInSamples
print " Hopsize: ", hopsize
print " Size of Fourier transforms: ", NFT
print " Number of iterations to be done: ", niter
print " Number of elements in WM: ", R
if is_stereo:
XR, F, N = stft(data[:, 0],
fs=Fs,
hopsize=hopsize,
window=sinebell(windowSizeInSamples),
nfft=NFT)
XL, F, N = stft(data[:, 1],
fs=Fs,
hopsize=hopsize,
window=sinebell(windowSizeInSamples),
nfft=NFT)
# SX is the power spectrogram:
## SXR = np.maximum(np.abs(XR) ** 2, 10 ** -8)
## SXL = np.maximum(np.abs(XL) ** 2, 10 ** -8)
#SXR = np.abs(XR) ** 2
#SXL = np.abs(XL) ** 2
SX = np.maximum((0.5 * np.abs(XR + XL))**2, eps)
else: # data is mono
X, F, N = stft(data,
fs=Fs,
hopsize=hopsize,
window=sinebell(windowSizeInSamples),
nfft=NFT)
SX = np.maximum(np.abs(X)**2, eps)
del data, F, N
# minimum and maximum F0 in glottal source spectra dictionary
minF0 = options.minF0
maxF0 = options.maxF0
F, N = SX.shape
stepNotes = options.stepNotes # this is the number of F0s within one semitone
K = int(
options.K_numFilters) # number of spectral shapes for the filter part
P = int(options.P_numAtomFilters
) # number of elements in dictionary of smooth filters
chirpPerF0 = 1 # number of chirped spectral shapes between each F0
# this feature should be further studied before
# we find a good way of doing that.
# Create the harmonic combs, for each F0 between minF0 and maxF0:
F0Table, WF0 = \
generate_WF0_chirped(minF0, maxF0, Fs, Nfft=NFT, \
stepNotes=stepNotes, \
lengthWindow=windowSizeInSamples, Ot=0.25, \
perF0=chirpPerF0, \
depthChirpInSemiTone=.15, loadWF0=True,\
analysisWindow='sinebell')
WF0 = WF0[0:F, :] # ensure same size as SX
NF0 = F0Table.size # number of harmonic combs
# Normalization:
WF0 = WF0 / np.outer(np.ones(F), np.amax(WF0, axis=0))
# Create the dictionary of smooth filters, for the filter part of
# the lead isntrument:
WGAMMA = generateHannBasis(F, NFT, Fs=Fs, frequencyScale='linear', \
numberOfBasis=P, overlap=.75)
if options.sal_output_file is None or not os.path.exists(
options.sal_output_file):
if displayEvolution:
plt.figure(1)
plt.clf()
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel(r'Frame number $n$', fontsize=16)
plt.ylabel(r'Leading source number $u$', fontsize=16)
plt.ion()
# plt.show()
## the following seems superfluous if mpl's backend is macosx...
## raw_input("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"\
## "!! Press Return to resume the program. !!\n"\
## "!! Be sure that the figure has been !!\n"\
## "!! already displayed, so that the !!\n"\
## "!! evolution of HF0 will be visible. !!\n"\
## "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
## section to estimate the melody, on monophonic algo:
# First round of parameter estimation:
if (HPS):
from scipy.signal import medfilt
if (is_stereo & stereoEstimation):
SXR = np.maximum(np.abs(XR)**2, eps)
SXL = np.maximum(np.abs(XL)**2, eps)
if (HPS):
SXR = medfilt(SXR, 3)
SXL = medfilt(SXL, 3)
alphaR, alphaL, HGAMMA, HPHI, HF0, betaR, betaL, HM, WM, recoError1 = SIMM.Stereo_SIMM(
# the data to be fitted to:
SXR,
SXL,
# the basis matrices for the spectral combs
WF0,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K,
numberOfAccompanimentSpectralShapes=R,
# putting only 2 elements in accompaniment for a start...
# if any, initial amplitude matrices for
HGAMMA0=None,
HPHI0=None,
HF00=None,
WM0=None,
HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter,
updateRulePower=1.,
stepNotes=stepNotes,
lambdaHF0=0.0 / (1.0 * SX.max()),
alphaHF0=0.9,
verbose=options.verbose,
displayEvolution=displayEvolution)
else:
if (HPS):
SX = medfilt(SX, 3)
HGAMMA, HPHI, HF0, HM, WM, recoError1 = SIMM.SIMM(
# the data to be fitted to:
SX,
# the basis matrices for the spectral combs
WF0,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K,
numberOfAccompanimentSpectralShapes=R,
# putting only 2 elements in accompaniment for a start...
# if any, initial amplitude matrices for
HGAMMA0=None,
HPHI0=None,
HF00=None,
WM0=None,
HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter,
updateRulePower=1.,
stepNotes=stepNotes,
lambdaHF0=0.0 / (1.0 * SX.max()),
alphaHF0=0.9,
verbose=options.verbose,
displayEvolution=displayEvolution)
if displayEvolution:
h2 = plt.figure(2)
plt.clf()
imageMatlab.imageM(20 * np.log10(HF0))
matMax = (20 * np.log10(HF0)).max()
matMed = np.median(20 * np.log10(HF0))
plt.clim([matMed - 100, matMax])
else:
print "Loading Salience from file to calculate Melody: " + options.sal_output_file
loaded = np.loadtxt(options.sal_output_file).T
times = [loaded[0, :]]
HF0 = loaded[1:, :]
# If vit_pitch_output_file is not null, do melody extraction with Viterbi
if not (options.vit_pitch_output_file is None):
print "Viterbi decoding"
# Viterbi decoding to estimate the predominant fundamental
# frequency line
# create transition probability matrix - adhoc parameter 'scale'
# TODO: use "learned" parameter scale (NB: after many trials,
# provided scale and parameterization seems robust)
scale = 1.0
transitions = np.exp(-np.floor(np.arange(0, NF0) / stepNotes) * scale)
cutoffnote = 2 * 5 * stepNotes
transitions[cutoffnote:] = transitions[cutoffnote - 1]
transitionMatrixF0 = np.zeros([NF0 + 1, NF0 + 1]) # toeplitz matrix
b = np.arange(NF0)
transitionMatrixF0[0:NF0, 0:NF0] = \
transitions[\
np.array(np.abs(np.outer(np.ones(NF0), b) \
- np.outer(b, np.ones(NF0))), dtype=int)]
pf_0 = transitions[cutoffnote - 1] * 10**(-90)
p0_0 = transitions[cutoffnote - 1] * 10**(-100)
p0_f = transitions[cutoffnote - 1] * 10**(-80)
transitionMatrixF0[0:NF0, NF0] = pf_0
transitionMatrixF0[NF0, 0:NF0] = p0_f
transitionMatrixF0[NF0, NF0] = p0_0
sumTransitionMatrixF0 = np.sum(transitionMatrixF0, axis=1)
transitionMatrixF0 = transitionMatrixF0 \
/ np.outer(sumTransitionMatrixF0, \
np.ones(NF0 + 1))
# prior probabilities, and setting the array for Viterbi tracking:
priorProbabilities = 1 / (NF0 + 1.0) * np.ones([NF0 + 1])
logHF0 = np.zeros([NF0 + 1, N])
normHF0 = np.amax(HF0, axis=0)
barHF0 = np.array(HF0)
logHF0[0:NF0, :] = np.log(barHF0)
logHF0[0:NF0, normHF0 == 0] = np.amin(logHF0[logHF0 > -np.Inf])
logHF0[NF0, :] = np.maximum(np.amin(logHF0[logHF0 > -np.Inf]), -100)
indexBestPath = viterbiTrackingArray(\
logHF0, np.log(priorProbabilities),
np.log(transitionMatrixF0), verbose=options.verbose)
if displayEvolution:
h2.hold(True)
plt.plot(indexBestPath, '-b')
h2.hold(False)
plt.axis('tight')
del logHF0
# detection of silences:
# computing the melody restricted F0 amplitude matrix HF00
# (which will be used as initial HF0 for further algo):
HF00 = np.zeros([NF0 * chirpPerF0, N])
scopeAllowedHF0 = 2.0 / 1.0
# computing indices for and around the melody indices,
# dim1index are indices along axis 0, and dim2index along axis 1
# of HF0:
# TODO: use numpy broadcasting to make this "clearer" (if possible...)
dim1index = np.array(\
np.maximum(\
np.minimum(\
np.outer(chirpPerF0 * indexBestPath,
np.ones(chirpPerF0 \
* (2 \
* int(np.floor(stepNotes / scopeAllowedHF0)) \
+ 1))) \
+ np.outer(np.ones(N),
np.arange(-chirpPerF0 \
* int(np.floor(stepNotes / scopeAllowedHF0)),
chirpPerF0 \
* int((np.floor(stepNotes / scopeAllowedHF0))) \
+ 1)),
chirpPerF0 * NF0 - 1),
0),
dtype=int).reshape(1, N * chirpPerF0 \
* (2 * int(np.floor(stepNotes / scopeAllowedHF0)) \
+ 1))
dim2index = np.outer(np.arange(N),
np.ones(chirpPerF0 \
* (2 * int(np.floor(stepNotes \
/ scopeAllowedHF0)) + 1), \
dtype=int)\
).reshape(1, N * chirpPerF0 \
* (2 * int(np.floor(stepNotes \
/ scopeAllowedHF0)) \
+ 1))
HF00[dim1index, dim2index] = HF0[dim1index, dim2index] # HF0.max()
HF00[:, indexBestPath == (NF0 - 1)] = 0.0
HF00[:, indexBestPath == 0] = 0.0
# remove frames with less than (100 thres_energy) % of total energy.
thres_energy = 0.000584
SF0 = np.maximum(np.dot(WF0, HF00), eps)
SPHI = np.maximum(np.dot(WGAMMA, np.dot(HGAMMA, HPHI)), eps)
SM = np.maximum(np.dot(WM, HM), eps)
hatSX = np.maximum(SPHI * SF0 + SM, eps)
energyMel = np.sum((((SPHI * SF0) / hatSX)**2) * SX, axis=0)
energyMelSorted = np.sort(energyMel)
energyMelCumul = np.cumsum(energyMelSorted)
energyMelCumulNorm = energyMelCumul / max(energyMelCumul[-1], eps)
# normalized to the maximum of energy:
# expressed in 0.01 times the percentage
ind_999 = np.nonzero(energyMelCumulNorm > thres_energy)[0][0]
if ind_999 is None:
ind_999 = N
if not os.path.isdir(os.path.dirname((options.vit_pitch_output_file))):
os.mkdir(os.path.dirname((options.vit_pitch_output_file)))
np.savetxt(options.vit_pitch_output_file + '.egy',
np.array(
[np.arange(N) * hopsize / np.double(Fs), energyMel]).T,
fmt='%10.5f')
# energyMel <= energyMelCumul[ind_999]?
melNotPresent = (energyMel <= energyMelCumulNorm[ind_999])
# edit: frames predicted as unvoiced will be given negative values
# indexBestPath[melNotPresent] = 0
freqMelody = F0Table[np.array(np.minimum(indexBestPath,
len(F0Table) - 1),
dtype=int)]
freqMelody[melNotPresent] = -freqMelody[melNotPresent]
if not os.path.exists(os.path.dirname(options.vit_pitch_output_file)):
os.makedirs(os.path.dirname(options.vit_pitch_output_file))
np.savetxt(options.vit_pitch_output_file,
np.array(
[np.arange(N) * hopsize / np.double(Fs), freqMelody]).T,
fmt='%10.7f')
times = np.array([np.arange(N) * hopsize / np.double(Fs)])
# Save salience file:
if not (options.sal_output_file is None):
if not os.path.exists(os.path.dirname(options.sal_output_file)):
os.makedirs(os.path.dirname(options.sal_output_file))
np.savetxt(options.sal_output_file,
np.concatenate((times, HF0), axis=0).T,
fmt='%10.6f')
# saveSPHI (timbre related)
saveSPHI = 0
if saveSPHI:
if not os.path.exists(
os.path.dirname(options.sal_output_file + '.SPHI')):
os.makedirs(os.path.dirname(options.sal_output_file))
WPHI = np.dot(WGAMMA, HGAMMA)
SPHI = np.dot(WPHI, HPHI)
np.savetxt(options.sal_output_file + '.SPHI',
np.concatenate((times, SPHI), axis=0).T,
fmt='%10.4f')
#np.savetxt(options.sal_output_file+'.WGAMMA',np.concatenate((times,WGAMMA),axis=0).T,fmt='%10.4f')
# return times[0],freqMelody,HF0
print "Done!"
return times[0], HF0, options
def main():
import optparse
usage = "usage: %prog [options] inputAudioFile"
parser = optparse.OptionParser(usage)
# Name of the output files:
parser.add_option(
"-v",
"--vocal-output-file",
dest="voc_output_file",
type="string",
help="name of the audio output file for the estimated\n"
"solo (vocal) part. \n"
"If None, appends _lead to inputAudioFile.",
default=None,
)
parser.add_option(
"-m",
"--music-output-file",
dest="mus_output_file",
type="string",
help="name of the audio output file for the estimated\n"
"music part.\n"
"If None, appends _acc to inputAudioFile.",
default=None,
)
parser.add_option(
"-p",
"--pitch-output-file",
dest="pitch_output_file",
type="string",
help="name of the output file for the estimated pitches.\n" "If None, appends _pitches to inputAudioFile",
default=None,
)
# Some more optional options:
parser.add_option(
"-d", "--with-display", dest="displayEvolution", action="store_true", help="display the figures", default=False
)
parser.add_option(
"-q", "--quiet", dest="verbose", action="store_false", help="use to quiet all output verbose", default=True
)
parser.add_option(
"-n",
"--dontseparate",
dest="separateSignals",
action="store_false",
help="Trigger this option if you only desire to " + "estimate the melody",
default=True,
)
parser.add_option("--nb-iterations", dest="nbiter", help="number of iterations", type="int", default=30)
parser.add_option(
"--window-size", dest="windowSize", type="float", default=0.04644, help="size of analysis windows, in s."
)
parser.add_option(
"--Fourier-size",
dest="fourierSize",
type="int",
default=None,
help="size of Fourier transforms, " "in samples.",
)
parser.add_option(
"--hopsize",
dest="hopsize",
type="float",
default=0.0058,
help="size of the hop between analysis windows, in s.",
)
parser.add_option(
"--nb-accElements", dest="R", type="float", default=40.0, help="number of elements for the accompaniment."
)
parser.add_option(
"--with-melody",
dest="melody",
type="string",
default=None,
help="provide the melody in a file named MELODY, " "with at each line: <time (s)><F0 (Hz)>.",
)
parser.add_option(
"--numAtomFilters",
dest="P_numAtomFilters",
type="int",
default=30,
help="Number of atomic filters - in WGAMMA.",
)
parser.add_option(
"--numFilters",
dest="K_numFilters",
type="int",
default=10,
help="Number of filters for decomposition - in WPHI",
)
parser.add_option(
"--min-F0-Freq", dest="minF0", type="float", default=100.0, help="Minimum of fundamental frequency F0."
)
parser.add_option(
"--max-F0-Freq", dest="maxF0", type="float", default=800.0, help="Maximum of fundamental frequency F0."
)
parser.add_option(
"--step-F0s", dest="stepNotes", type="int", default=20, help="Number of F0s in dictionary for each semitone."
)
(options, args) = parser.parse_args()
if len(args) != 1:
parser.error("incorrect number of arguments, use option -h for help.")
displayEvolution = options.displayEvolution
if displayEvolution:
import matplotlib.pyplot as plt
import imageMatlab
## plt.rc('text', usetex=True)
plt.rc("image", cmap="jet") ## gray_r
plt.ion()
# Compulsory option: name of the input file:
inputAudioFile = args[0]
if inputAudioFile[-4:] != ".wav":
raise ValueError("File not WAV file? Only WAV format support, for now...")
if options.mus_output_file is None:
options.mus_output_file = inputAudioFile[:-4] + "_acc.wav"
if options.voc_output_file is None:
options.voc_output_file = inputAudioFile[:-4] + "_lead.wav"
if options.pitch_output_file is None:
options.pitch_output_file = inputAudioFile[:-4] + "_pitches.txt"
print "Writing the different following output files:"
print " separated lead in", options.voc_output_file
print " separated accompaniment in", options.mus_output_file
print " separated lead + unvoc in", options.voc_output_file[:-4] + "_VUIMM.wav"
print " separated acc - unvoc in", options.mus_output_file[:-4] + "_VUIMM.wav"
print " estimated pitches in", options.pitch_output_file
Fs, data = wav.read(inputAudioFile)
# data = np.double(data) / 32768.0 # makes data vary from -1 to 1
scaleData = 1.2 * data.max() # to rescale the data.
dataType = data.dtype
data = np.double(data) / scaleData # makes data vary from -1 to 1
is_stereo = True
if data.shape[0] == data.size: # data is multi-channel
print "The audio file is not stereo. Making stereo out of mono."
print "(You could also try the older separateLead.py...)"
is_stereo = False
# data = np.vstack([data,data]).T
# raise ValueError("number of dimensions of the input not 2")
if is_stereo and data.shape[1] != 2:
print "The data is multichannel, but not stereo... \n"
print "Unfortunately this program does not scale well. Data is \n"
print "reduced to its 2 first channels.\n"
data = data[:, 0:2]
# Processing the options:
windowSizeInSamples = nextpow2(np.round(options.windowSize * Fs))
hopsize = np.round(options.hopsize * Fs)
if hopsize != windowSizeInSamples / 8:
# print "Overriding given hopsize to use 1/8th of window size"
# hopsize = windowSizeInSamples/8
warnings.warn("Chosen hopsize: " + str(hopsize) + ", while windowsize: " + str(windowSizeInSamples))
if options.fourierSize is None:
NFT = windowSizeInSamples
else:
NFT = options.fourierSize
# number of iterations for each parameter estimation step:
niter = options.nbiter
# number of spectral shapes for the accompaniment
R = options.R
eps = 10 ** -9
if options.verbose:
print "Some parameter settings:"
print " Size of analysis windows: ", windowSizeInSamples
print " Hopsize: ", hopsize
print " Size of Fourier transforms: ", NFT
print " Number of iterations to be done: ", niter
print " Number of elements in WM: ", R
if is_stereo:
XR, F, N = stft(data[:, 0], fs=Fs, hopsize=hopsize, window=sinebell(windowSizeInSamples), nfft=NFT)
XL, F, N = stft(data[:, 1], fs=Fs, hopsize=hopsize, window=sinebell(windowSizeInSamples), nfft=NFT)
# SX is the power spectrogram:
## SXR = np.maximum(np.abs(XR) ** 2, 10 ** -8)
## SXL = np.maximum(np.abs(XL) ** 2, 10 ** -8)
# SXR = np.abs(XR) ** 2
# SXL = np.abs(XL) ** 2
SX = np.maximum((0.5 * np.abs(XR + XL)) ** 2, eps)
else: # data is mono
X, F, N = stft(data, fs=Fs, hopsize=hopsize, window=sinebell(windowSizeInSamples), nfft=NFT)
SX = np.maximum(np.abs(X) ** 2, eps)
del data, F, N
# TODO: also process these as options:
# minimum and maximum F0 in glottal source spectra dictionary
minF0 = options.minF0
maxF0 = options.maxF0
F, N = SX.shape
stepNotes = options.stepNotes # this is the number of F0s within one semitone
K = options.K_numFilters # number of spectral shapes for the filter part
P = options.P_numAtomFilters # number of elements in dictionary of smooth filters
chirpPerF0 = 1 # number of chirped spectral shapes between each F0
# this feature should be further studied before
# we find a good way of doing that.
# Create the harmonic combs, for each F0 between minF0 and maxF0:
F0Table, WF0 = generate_WF0_chirped(
minF0,
maxF0,
Fs,
Nfft=NFT,
stepNotes=stepNotes,
lengthWindow=windowSizeInSamples,
Ot=0.25,
perF0=chirpPerF0,
depthChirpInSemiTone=0.15,
loadWF0=True,
analysisWindow="sinebell",
)
WF0 = WF0[0:F, :] # ensure same size as SX
NF0 = F0Table.size # number of harmonic combs
# Normalization:
WF0 = WF0 / np.outer(np.ones(F), np.amax(WF0, axis=0))
# Create the dictionary of smooth filters, for the filter part of
# the lead isntrument:
WGAMMA = generateHannBasis(F, NFT, Fs=Fs, frequencyScale="linear", numberOfBasis=P, overlap=0.75)
if displayEvolution:
plt.figure(1)
plt.clf()
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel(r"Frame number $n$", fontsize=16)
plt.ylabel(r"Leading source number $u$", fontsize=16)
plt.ion()
# plt.show()
## the following seems superfluous if mpl's backend is macosx...
## raw_input("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"\
## "!! Press Return to resume the program. !!\n"\
## "!! Be sure that the figure has been !!\n"\
## "!! already displayed, so that the !!\n"\
## "!! evolution of HF0 will be visible. !!\n"\
## "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
if options.melody is None:
## section to estimate the melody, on monophonic algo:
# First round of parameter estimation:
HGAMMA, HPHI, HF0, HM, WM, recoError1 = SIMM.SIMM(
# the data to be fitted to:
SX,
# the basis matrices for the spectral combs
WF0,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K,
numberOfAccompanimentSpectralShapes=R,
# putting only 2 elements in accompaniment for a start...
# if any, initial amplitude matrices for
HGAMMA0=None,
HPHI0=None,
HF00=None,
WM0=None,
HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter,
updateRulePower=1.0,
stepNotes=stepNotes,
lambdaHF0=0.0 / (1.0 * SX.max()),
alphaHF0=0.9,
verbose=options.verbose,
displayEvolution=displayEvolution,
)
if displayEvolution:
h2 = plt.figure(2)
plt.clf()
imageMatlab.imageM(20 * np.log10(HF0))
matMax = (20 * np.log10(HF0)).max()
matMed = np.median(20 * np.log10(HF0))
plt.clim([matMed - 100, matMax])
# Viterbi decoding to estimate the predominant fundamental
# frequency line
# create transition probability matrix - adhoc parameter 'scale'
# TODO: use "learned" parameter scale (NB: after many trials,
# provided scale and parameterization seems robust)
scale = 1.0
transitions = np.exp(-np.floor(np.arange(0, NF0) / stepNotes) * scale)
cutoffnote = 2 * 5 * stepNotes
transitions[cutoffnote:] = transitions[cutoffnote - 1]
transitionMatrixF0 = np.zeros([NF0 + 1, NF0 + 1]) # toeplitz matrix
b = np.arange(NF0)
transitionMatrixF0[0:NF0, 0:NF0] = transitions[
np.array(np.abs(np.outer(np.ones(NF0), b) - np.outer(b, np.ones(NF0))), dtype=int)
]
pf_0 = transitions[cutoffnote - 1] * 10 ** (-90)
p0_0 = transitions[cutoffnote - 1] * 10 ** (-100)
p0_f = transitions[cutoffnote - 1] * 10 ** (-80)
transitionMatrixF0[0:NF0, NF0] = pf_0
transitionMatrixF0[NF0, 0:NF0] = p0_f
transitionMatrixF0[NF0, NF0] = p0_0
sumTransitionMatrixF0 = np.sum(transitionMatrixF0, axis=1)
transitionMatrixF0 = transitionMatrixF0 / np.outer(sumTransitionMatrixF0, np.ones(NF0 + 1))
# prior probabilities, and setting the array for Viterbi tracking:
priorProbabilities = 1 / (NF0 + 1.0) * np.ones([NF0 + 1])
logHF0 = np.zeros([NF0 + 1, N])
normHF0 = np.amax(HF0, axis=0)
barHF0 = np.array(HF0)
logHF0[0:NF0, :] = np.log(barHF0)
logHF0[0:NF0, normHF0 == 0] = np.amin(logHF0[logHF0 > -np.Inf])
logHF0[NF0, :] = np.maximum(np.amin(logHF0[logHF0 > -np.Inf]), -100)
indexBestPath = viterbiTrackingArray(
logHF0, np.log(priorProbabilities), np.log(transitionMatrixF0), verbose=options.verbose
)
if displayEvolution:
h2.hold(True)
plt.plot(indexBestPath, "-b")
h2.hold(False)
plt.axis("tight")
del logHF0
# detection of silences:
# computing the melody restricted F0 amplitude matrix HF00
# (which will be used as initial HF0 for further algo):
HF00 = np.zeros([NF0 * chirpPerF0, N])
scopeAllowedHF0 = 2.0 / 1.0
# computing indices for and around the melody indices,
# dim1index are indices along axis 0, and dim2index along axis 1
# of HF0:
# TODO: use numpy broadcasting to make this "clearer" (if possible...)
dim1index = np.array(
np.maximum(
np.minimum(
np.outer(
chirpPerF0 * indexBestPath,
np.ones(chirpPerF0 * (2 * np.floor(stepNotes / scopeAllowedHF0) + 1)),
)
+ np.outer(
np.ones(N),
np.arange(
-chirpPerF0 * np.floor(stepNotes / scopeAllowedHF0),
chirpPerF0 * (np.floor(stepNotes / scopeAllowedHF0) + 1),
),
),
chirpPerF0 * NF0 - 1,
),
0,
),
dtype=int,
).reshape(1, N * chirpPerF0 * (2 * np.floor(stepNotes / scopeAllowedHF0) + 1))
dim2index = np.outer(
np.arange(N), np.ones(chirpPerF0 * (2 * np.floor(stepNotes / scopeAllowedHF0) + 1), dtype=int)
).reshape(1, N * chirpPerF0 * (2 * np.floor(stepNotes / scopeAllowedHF0) + 1))
HF00[dim1index, dim2index] = HF0[dim1index, dim2index] # HF0.max()
HF00[:, indexBestPath == (NF0 - 1)] = 0.0
HF00[:, indexBestPath == 0] = 0.0
# remove frames with less than (100 thres_energy) % of total energy.
thres_energy = 0.000584
SF0 = np.maximum(np.dot(WF0, HF00), eps)
SPHI = np.maximum(np.dot(WGAMMA, np.dot(HGAMMA, HPHI)), eps)
SM = np.maximum(np.dot(WM, HM), eps)
hatSX = np.maximum(SPHI * SF0 + SM, eps)
energyMel = np.sum((((SPHI * SF0) / hatSX) ** 2) * SX, axis=0)
energyMelSorted = np.sort(energyMel)
energyMelCumul = np.cumsum(energyMelSorted)
energyMelCumulNorm = energyMelCumul / max(energyMelCumul[-1], eps)
# normalized to the maximum of energy:
# expressed in 0.01 times the percentage
ind_999 = np.nonzero(energyMelCumulNorm > thres_energy)[0][0]
if ind_999 is None:
ind_999 = N
melNotPresent = energyMel <= energyMelCumulNorm[ind_999]
indexBestPath[melNotPresent] = 0
else:
## take the provided melody line:
# load melody from file:
melodyFromFile = np.loadtxt(options.melody)
sizeProvidedMel = melodyFromFile.shape
if len(sizeProvidedMel) == 1:
print "The melody should be provided as <Time (s)><F0 (Hz)>."
raise ValueError("Bad melody format")
melTimeStamps = melodyFromFile[:, 0] # + 1024 / np.double(Fs)
melFreqHz = melodyFromFile[:, 1]
if minF0 > melFreqHz[melFreqHz > 40.0].min() or maxF0 < melFreqHz.max():
minF0 = melFreqHz[melFreqHz > 40.0].min() * 0.97
maxF0 = np.maximum(melFreqHz.max() * 1.03, 2 * minF0 * 1.03)
print "Recomputing the source basis for "
print "minF0 = ", minF0, "Hz and maxF0 = ", maxF0, "Hz."
# Create the harmonic combs, for each F0 between minF0 and maxF0:
F0Table, WF0 = generate_WF0_chirped(
minF0,
maxF0,
Fs,
Nfft=NFT,
stepNotes=stepNotes,
lengthWindow=windowSizeInSamples,
Ot=0.25,
perF0=chirpPerF0,
depthChirpInSemiTone=0.15,
)
WF0 = WF0[0:F, :] # ensure same size as SX
NF0 = F0Table.size # number of harmonic combs
# Normalization:
WF0 = WF0 / np.outer(np.ones(F), np.amax(WF0, axis=0))
sigTimeStamps = np.arange(N) * hopsize / np.double(Fs)
distMatTimeStamps = np.abs(
np.outer(np.ones(sizeProvidedMel[0]), sigTimeStamps) - np.outer(melTimeStamps, np.ones(N))
)
minDistTimeStamps = distMatTimeStamps.argmin(axis=0)
f0BestPath = melFreqHz[minDistTimeStamps]
distMatF0 = np.abs(np.outer(np.ones(NF0), f0BestPath) - np.outer(F0Table, np.ones(N)))
indexBestPath = distMatF0.argmin(axis=0)
# setting silences to 0, with tolerance = 1/2 window length
indexBestPath[distMatTimeStamps[minDistTimeStamps, range(N)] >= 0.5 * options.windowSize] = 0
indexBestPath[f0BestPath <= 0] = 0
freqMelody = F0Table[np.array(indexBestPath, dtype=int)]
freqMelody[indexBestPath == 0] = -freqMelody[indexBestPath == 0]
np.savetxt(options.pitch_output_file, np.array([np.arange(N) * hopsize / np.double(Fs), freqMelody]).T)
# If separation is required:
if options.separateSignals:
# Second round of parameter estimation, with specific
# initial HF00:
HF00 = np.zeros([NF0 * chirpPerF0, N])
scopeAllowedHF0 = 2.0 / 1.0
# indexes for HF00:
# TODO: reprogram this with a 'where'?...
dim1index = np.array(
np.maximum(
np.minimum(
np.outer(
chirpPerF0 * indexBestPath,
np.ones(chirpPerF0 * (2 * np.floor(stepNotes / scopeAllowedHF0) + 1)),
)
+ np.outer(
np.ones(N),
np.arange(
-chirpPerF0 * np.floor(stepNotes / scopeAllowedHF0),
chirpPerF0 * (np.floor(stepNotes / scopeAllowedHF0) + 1),
),
),
chirpPerF0 * NF0 - 1,
),
0,
),
dtype=int,
)
dim1index = dim1index[indexBestPath != 0, :]
## dim1index = dim1index.reshape(1, N * chirpPerF0 \
## * (2 * np.floor(stepNotes / scopeAllowedHF0) \
## + 1))
dim1index = dim1index.reshape(1, dim1index.size)
dim2index = np.outer(
np.arange(N), np.ones(chirpPerF0 * (2 * np.floor(stepNotes / scopeAllowedHF0) + 1), dtype=int)
)
dim2index = dim2index[indexBestPath != 0, :]
dim2index = dim2index.reshape(1, dim2index.size)
## dim2index.reshape(1, N * chirpPerF0 \
## * (2 * np.floor(stepNotes \
## / scopeAllowedHF0) \
## + 1))
HF00[dim1index, dim2index] = 1 # HF0.max()
HF00[:, indexBestPath == (NF0 - 1)] = 0.0
HF00[:, indexBestPath == 0] = 0.0
WF0effective = WF0
HF00effective = HF00
if options.melody is None:
del HF0, HGAMMA, HPHI, HM, WM, HF00
if is_stereo:
del SX
SXR = np.maximum(np.abs(XR) ** 2, eps)
SXL = np.maximum(np.abs(XL) ** 2, eps)
alphaR, alphaL, HGAMMA, HPHI, HF0, betaR, betaL, HM, WM, recoError2 = SIMM.Stereo_SIMM(
# the data to be fitted to:
SXR,
SXL,
# the basis matrices for the spectral combs
WF0effective,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K,
numberOfAccompanimentSpectralShapes=R,
# if any, initial amplitude matrices for
HGAMMA0=None,
HPHI0=None,
HF00=HF00effective,
WM0=None,
HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter,
updateRulePower=1.0,
stepNotes=stepNotes,
lambdaHF0=0.0 / (1.0 * SXR.max()),
alphaHF0=0.9,
verbose=options.verbose,
displayEvolution=displayEvolution,
)
WPHI = np.dot(WGAMMA, HGAMMA)
SPHI = np.dot(WPHI, HPHI)
SF0 = np.dot(WF0effective, HF0)
hatSXR = (alphaR ** 2) * SF0 * SPHI + np.dot(np.dot(WM, betaR ** 2), HM)
hatSXL = (alphaL ** 2) * SF0 * SPHI + np.dot(np.dot(WM, betaL ** 2), HM)
hatVR = (alphaR ** 2) * SPHI * SF0 / hatSXR * XR
vestR = istft(hatVR, hopsize=hopsize, nfft=NFT, window=sinebell(windowSizeInSamples)) / 4.0
hatVR = (alphaL ** 2) * SPHI * SF0 / hatSXL * XL
vestL = istft(hatVR, hopsize=hopsize, nfft=NFT, window=sinebell(windowSizeInSamples)) / 4.0
# scikits.audiolab.wavwrite(np.array([vestR,vestL]).T, \
# options.voc_output_file, Fs)
vestR = np.array(np.round(vestR * scaleData), dtype=dataType)
vestL = np.array(np.round(vestL * scaleData), dtype=dataType)
wav.write(options.voc_output_file, Fs, np.array([vestR, vestL]).T)
# wav.write(options.voc_output_file, Fs, \
# np.int16(32768.0 * np.array([vestR,vestL]).T))
hatMR = (np.dot(np.dot(WM, betaR ** 2), HM)) / hatSXR * XR
mestR = istft(hatMR, hopsize=hopsize, nfft=NFT, window=sinebell(windowSizeInSamples)) / 4.0
hatMR = (np.dot(np.dot(WM, betaL ** 2), HM)) / hatSXL * XL
mestL = istft(hatMR, hopsize=hopsize, nfft=NFT, window=sinebell(windowSizeInSamples)) / 4.0
# scikits.audiolab.wavwrite(np.array([mestR,mestL]).T, \
# options.mus_output_file, Fs)
mestR = np.array(np.round(mestR * scaleData), dtype=dataType)
mestL = np.array(np.round(mestL * scaleData), dtype=dataType)
wav.write(options.mus_output_file, Fs, np.array([mestR, mestL]).T)
# wav.write(options.mus_output_file, Fs, \
# np.int16(32768.0 * np.array([mestR,mestL]).T))
del hatMR, mestL, vestL, vestR, mestR, hatVR, hatSXR, hatSXL, SPHI, SF0
# adding the unvoiced part in the source basis:
WUF0 = np.hstack([WF0, np.ones([WF0.shape[0], 1])])
HUF0 = np.vstack([HF0, np.ones([1, HF0.shape[1]])])
## HUF0[-1,:] = HF0.sum(axis=0) # should we do this?
alphaR, alphaL, HGAMMA, HPHI, HF0, betaR, betaL, HM, WM, recoError3 = SIMM.Stereo_SIMM(
# the data to be fitted to:
SXR,
SXL,
# the basis matrices for the spectral combs
WUF0,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K,
numberOfAccompanimentSpectralShapes=R,
# if any, initial amplitude matrices for
HGAMMA0=HGAMMA,
HPHI0=HPHI,
HF00=HUF0,
WM0=None, # WM,
HM0=None, # HM,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter,
updateRulePower=1.0,
stepNotes=stepNotes,
lambdaHF0=0.0 / (1.0 * SXR.max()),
alphaHF0=0.9,
verbose=options.verbose,
displayEvolution=displayEvolution,
updateHGAMMA=False,
)
WPHI = np.dot(WGAMMA, HGAMMA)
SPHI = np.dot(WPHI, HPHI)
SF0 = np.dot(WUF0, HF0)
hatSXR = (alphaR ** 2) * SF0 * SPHI + np.dot(np.dot(WM, betaR ** 2), HM)
hatSXL = (alphaL ** 2) * SF0 * SPHI + np.dot(np.dot(WM, betaL ** 2), HM)
hatVR = (alphaR ** 2) * SPHI * SF0 / hatSXR * XR
vestR = istft(hatVR, hopsize=hopsize, nfft=NFT, window=sinebell(windowSizeInSamples)) / 4.0
hatVR = (alphaL ** 2) * SPHI * SF0 / hatSXL * XL
vestL = istft(hatVR, hopsize=hopsize, nfft=NFT, window=sinebell(windowSizeInSamples)) / 4.0
outputFileName = options.voc_output_file[:-4] + "_VUIMM.wav"
vestR = np.array(np.round(vestR * scaleData), dtype=dataType)
vestL = np.array(np.round(vestL * scaleData), dtype=dataType)
wav.write(outputFileName, Fs, np.array([vestR, vestL]).T)
hatMR = (np.dot(np.dot(WM, betaR ** 2), HM)) / hatSXR * XR
mestR = istft(hatMR, hopsize=hopsize, nfft=NFT, window=sinebell(windowSizeInSamples)) / 4.0
hatMR = (np.dot(np.dot(WM, betaL ** 2), HM)) / hatSXL * XL
mestL = istft(hatMR, hopsize=hopsize, nfft=NFT, window=sinebell(windowSizeInSamples)) / 4.0
outputFileName = options.mus_output_file[:-4] + "_VUIMM.wav"
mestR = np.array(np.round(mestR * scaleData), dtype=dataType)
mestL = np.array(np.round(mestL * scaleData), dtype=dataType)
wav.write(outputFileName, Fs, np.array([mestR, mestL]).T)
else:
# running on monophonic data:
HGAMMA, HPHI, HF0, HM, WM, recoError1 = SIMM.SIMM(
# the data to be fitted to:
SX,
# the basis matrices for the spectral combs
WF0effective,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K,
numberOfAccompanimentSpectralShapes=R,
# putting only 2 elements in accompaniment for a start...
# if any, initial amplitude matrices for
HGAMMA0=None,
HPHI0=None,
HF00=HF00effective,
WM0=None,
HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter,
updateRulePower=1.0,
stepNotes=stepNotes,
lambdaHF0=0.0 / (1.0 * SX.max()),
alphaHF0=0.9,
verbose=options.verbose,
displayEvolution=displayEvolution,
)
WPHI = np.dot(WGAMMA, HGAMMA)
SPHI = np.dot(WPHI, HPHI)
SF0 = np.dot(WF0effective, HF0)
SM = np.dot(WM, HM)
hatSX = SF0 * SPHI + SM
hatV = SPHI * SF0 / hatSX * X
vest = istft(hatV, hopsize=hopsize, nfft=NFT, window=sinebell(windowSizeInSamples)) / 4.0
vest = np.array(np.round(vest * scaleData), dtype=dataType)
wav.write(options.voc_output_file, Fs, vest)
hatM = SM / hatSX * X
mest = istft(hatM, hopsize=hopsize, nfft=NFT, window=sinebell(windowSizeInSamples)) / 4.0
mest = np.array(np.round(mest * scaleData), dtype=dataType)
wav.write(options.mus_output_file, Fs, mest)
del hatM, vest, mest, hatV, hatSX, SPHI, SF0
# adding the unvoiced part in the source basis:
WUF0 = np.hstack([WF0, np.ones([WF0.shape[0], 1])])
HUF0 = np.vstack([HF0, np.ones([1, HF0.shape[1]])])
## HUF0[-1,:] = HF0.sum(axis=0) # should we do this?
HGAMMA, HPHI, HF0, HM, WM, recoError1 = SIMM.SIMM(
# the data to be fitted to:
SX,
# the basis matrices for the spectral combs
WUF0,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K,
numberOfAccompanimentSpectralShapes=R,
# putting only 2 elements in accompaniment for a start...
# if any, initial amplitude matrices for
HGAMMA0=HGAMMA,
HPHI0=HPHI,
HF00=HUF0,
WM0=None,
HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter,
updateRulePower=1.0,
stepNotes=stepNotes,
lambdaHF0=0.0 / (1.0 * SX.max()),
alphaHF0=0.9,
verbose=options.verbose,
displayEvolution=displayEvolution,
updateHGAMMA=False,
)
WPHI = np.dot(WGAMMA, HGAMMA)
SPHI = np.dot(WPHI, HPHI)
SF0 = np.dot(WUF0, HF0)
SM = np.dot(WM, HM)
hatSX = SF0 * SPHI + SM
hatV = SPHI * SF0 / hatSX * X
vest = istft(hatV, hopsize=hopsize, nfft=NFT, window=sinebell(windowSizeInSamples)) / 4.0
vest = np.array(np.round(vest * scaleData), dtype=dataType)
outputFileName = options.voc_output_file[:-4] + "_VUIMM.wav"
wav.write(outputFileName, Fs, vest)
hatM = SM / hatSX * X
mest = istft(hatM, hopsize=hopsize, nfft=NFT, window=sinebell(windowSizeInSamples)) / 4.0
mest = np.array(np.round(mest * scaleData), dtype=dataType)
outputFileName = options.mus_output_file[:-4] + "_VUIMM.wav"
wav.write(outputFileName, Fs, mest)
if displayEvolution:
plt.close("all")
print "Done!"
def main():
import optparse
usage = "usage: %prog [options] inputAudioFile"
parser = optparse.OptionParser(usage)
# Name of the output files:
parser.add_option("-v", "--vocal-output-file",
dest="voc_output_file", type="string",
help="name of the audio output file for the estimated\nsolo (vocal) part",
default="estimated_solo.wav")
parser.add_option("-m", "--music-output-file",
dest="mus_output_file", type="string",
help="name of the audio output file for the estimated\nmusic part",
default="estimated_music.wav")
parser.add_option("-p", "--pitch-output-file",
dest="pitch_output_file", type="string",
help="name of the output file for the estimated pitches",
default="pitches.txt")
# Some more optional options:
parser.add_option("-d", "--with-display", dest="displayEvolution",
action="store_true",help="display the figures",
default=False)
parser.add_option("-q", "--quiet", dest="verbose",
action="store_false",
help="use to quiet all output verbose",
default=True)
parser.add_option("--nb-iterations", dest="nbiter",
help="number of iterations", type="int",
default=50)
parser.add_option("--window-size", dest="windowSize", type="float",
default=0.04644,help="size of analysis windows, in s.")
parser.add_option("--Fourier-size", dest="fourierSize", type="int",
default=2048, help="size of Fourier transforms, in samples.")
parser.add_option("--hopsize", dest="hopsize", type="float",
default=0.0058,
help="size of the hop between analysis windows, in s.")
(options, args) = parser.parse_args()
if len(args) != 1:
parser.error("incorrect number of arguments, use option -h for help.")
displayEvolution = options.displayEvolution
if displayEvolution:
import matplotlib.pyplot as plt
import imageMatlab
plt.rc('text', usetex=True)
plt.rc('image',cmap='gray_r')
plt.ion()
# Compulsory option: name of the input file:
inputAudioFile = args[0]
fs, data = wav.read(inputAudioFile)
#data, fs, enc = scikits.audiolab.wavread(inputAudioFile)
if data.shape[0] != data.size: # data is multi-channel
data = np.mean(data,axis=1)
# Processing the options:
windowSizeInSamples = np.round(options.windowSize * fs)
hopsize = np.round(options.hopsize * fs)
NFT = options.fourierSize
niter = options.nbiter
if options.verbose:
print "Size of analysis windows: ", windowSizeInSamples, "\n"
print "Hopsize: ", hopsize, "\n"
print "Size of Fourier transforms: ", NFT, "\n"
print "Number of iterations to be done: ", niter, "\n"
X, F, N = stft(data, fs=fs, hopsize=hopsize,
window=sinebell(windowSizeInSamples), nfft=NFT)
# SX is the power spectrogram:
SX = np.maximum(np.abs(X) ** 2, 10 ** -8)
del data, F, N
# TODO: also process these as options:
minF0 = 100
maxF0 = 800
Fs = fs
F, N = SX.shape
stepNotes = 20 # this is the number of F0s within one semitone
K = 50 # number of spectral shapes for the filter part
R = 40 # number of spectral shapes for the accompaniment
P = 30 # number of elements in dictionary of smooth filters
chirpPerF0 = 1 # number of chirped spectral shapes between each F0
# this feature should be further studied before
# we find a good way of doing that.
# Create the harmonic combs, for each F0 between minF0 and maxF0:
F0Table, WF0 = \
generate_WF0_chirped(minF0, maxF0, Fs, Nfft=2048, \
stepNotes=stepNotes, \
lengthWindow=2048, Ot=0.25, \
perF0=chirpPerF0, \
depthChirpInSemiTone=.15)
WF0 = WF0[0:F, :] # ensure same size as SX
NF0 = F0Table.size # number of harmonic combs
# Normalization:
WF0 = WF0 / np.outer(np.ones(F), np.amax(WF0, axis=0))
# Create the dictionary of smooth filters, for the filter part of
# the lead isntrument:
WGAMMA = generateHannBasis(F, 2048, Fs=fs, frequencyScale='linear', \
numberOfBasis=P, overlap=.75)
if displayEvolution:
plt.figure(1);plt.clf()
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel(r'Frame number $n$', fontsize=16)
plt.ylabel(r'Leading source number $u$', fontsize=16)
plt.ion()
# plt.show()
raw_input("Press Return to resume the program. \nBe sure that the figure has been already displayed, so that the evolution of HF0 will be visible. ")
# First round of parameter estimation:
HGAMMA, HPHI, HF0, HM, WM, recoError1 = SIMM.SIMM(
# the data to be fitted to:
SX,
# the basis matrices for the spectral combs
WF0,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K, numberOfAccompanimentSpectralShapes=R,
# putting only 2 elements in accompaniment for a start...
# if any, initial amplitude matrices for
HGAMMA0=None, HPHI0=None,
HF00=None,
WM0=None, HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter, updateRulePower=1.,
stepNotes=stepNotes,
lambdaHF0 = 0.0 / (1.0 * SX.max()), alphaHF0=0.9,
verbose=options.verbose, displayEvolution=displayEvolution)
if displayEvolution:
plt.figure(3);plt.clf()
plt.subplot(221)
plt.plot(db(np.dot(WGAMMA, HGAMMA[:, 0])))
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.ylim([-30, 0])
plt.axis("tight")
plt.subplot(222)
plt.plot(db(np.dot(WGAMMA, HGAMMA[:,1])))
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.ylim([-30, 0])
plt.axis("tight")
plt.subplot(223)
plt.plot(db(np.dot(WGAMMA, HGAMMA[:, 2])))
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.ylim([-30, 0])
plt.axis("tight")
plt.subplot(224)
plt.plot(db(np.dot(WGAMMA, HGAMMA[:, 3])))
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.ylim([-30, 0])
plt.axis("tight")
plt.figure(4);plt.clf()
imageMatlab.imageM(db(np.dot(np.dot(WGAMMA, HGAMMA), HPHI)))
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel(r'Frame number $n$', fontsize=16)
plt.ylabel(r'Frequency bin number $f$', fontsize=16)
cb = plt.colorbar(fraction=0.04)
plt.axes(cb.ax)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.figure(5);plt.clf()
imageMatlab.imageM(db(HF0), vmin=-100, cmap=plt.cm.gray_r)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel(r'Frame number $n$', fontsize=16)
plt.ylabel(r'Leading source number $u$', fontsize=16)
# plt.xlim([3199.5, 3500.5]) # For detailed picture of HF0
cb = plt.colorbar(fraction=0.04)
plt.axes(cb.ax)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.figure(6);plt.clf()
imageMatlab.imageM(db(np.dot(WM, HM)), vmin=-50)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel(r'Frame number $n$', fontsize=16)
plt.ylabel(r'Frequency bin number $f$', fontsize=16)
cb = plt.colorbar(fraction=0.04)
plt.axes(cb.ax)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.figure(7);plt.clf()
imageMatlab.imageM(db(WM), vmin=-50)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel(r'Element number $r$', fontsize=16)
plt.ylabel(r'Frequency bin number $f$', fontsize=16)
cb = plt.colorbar(fraction=0.04)
plt.axes(cb.ax)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if displayEvolution:
h2 = plt.figure(2);plt.clf();
imageMatlab.imageM(20 * np.log10(HF0))
matMax = (20 * np.log10(HF0)).max()
matMed = np.median(20 * np.log10(HF0))
plt.clim([matMed - 100, matMax])
# Viterbi decoding to estimate the predominant fundamental
# frequency line
scale = 1.0
transitions = np.exp(-np.floor(np.arange(0,NF0) / stepNotes) * scale)
cutoffnote = 2 * 5 * stepNotes
transitions[cutoffnote:] = transitions[cutoffnote - 1]
transitionMatrixF0 = np.zeros([NF0 + 1, NF0 + 1]) # toeplitz matrix
b = np.arange(NF0)
transitionMatrixF0[0:NF0, 0:NF0] = \
transitions[\
np.array(np.abs(np.outer(np.ones(NF0), b) \
- np.outer(b, np.ones(NF0))), dtype=int)]
pf_0 = transitions[cutoffnote - 1] * 10 ** (-90)
p0_0 = transitions[cutoffnote - 1] * 10 ** (-100)
p0_f = transitions[cutoffnote - 1] * 10 ** (-80)
transitionMatrixF0[0:NF0, NF0] = pf_0
transitionMatrixF0[NF0, 0:NF0] = p0_f
transitionMatrixF0[NF0, NF0] = p0_0
sumTransitionMatrixF0 = np.sum(transitionMatrixF0, axis=1)
transitionMatrixF0 = transitionMatrixF0 \
/ np.outer(sumTransitionMatrixF0, \
np.ones(NF0 + 1))
priorProbabilities = 1 / (NF0 + 1.0) * np.ones([NF0 + 1])
logHF0 = np.zeros([NF0 + 1, N])
normHF0 = np.amax(HF0, axis=0)
barHF0 = np.array(HF0)
logHF0[0:NF0, :] = np.log(barHF0)
logHF0[0:NF0, normHF0==0] = np.amin(logHF0[logHF0>-np.Inf])
logHF0[NF0, :] = np.maximum(np.amin(logHF0[logHF0>-np.Inf]),-100)
indexBestPath = viterbiTrackingArray(\
logHF0, np.log(priorProbabilities), np.log(transitionMatrixF0))
np.savetxt(options.pitch_output_file,
np.array([np.arange(N)*options.hopsize,
F0Table[np.array(indexBestPath,dtype=int)]]).T)
if displayEvolution:
h2.hold(True)
plt.plot(indexBestPath, '-b')
h2.hold(False)
plt.axis('tight')
raw_input("Press Return to resume the program...")
del logHF0
# Second round of parameter estimation, with specific
# initial HF00:
HF00 = np.zeros([NF0 * chirpPerF0, N])
scopeAllowedHF0 = 1.0 / 1.0
# indexes for HF00:
# TODO: reprogram this with a 'where'?...
dim1index = np.array(\
np.maximum(\
np.minimum(\
np.outer(chirpPerF0 * indexBestPath,
np.ones(chirpPerF0 \
* (2 \
* np.floor(stepNotes / scopeAllowedHF0) \
+ 1))) \
+ np.outer(np.ones(N),
np.arange(-chirpPerF0 \
* np.floor(stepNotes / scopeAllowedHF0),
chirpPerF0 \
* (np.floor(stepNotes / scopeAllowedHF0) \
+ 1))),
chirpPerF0 * NF0 - 1),
0),
dtype=int).reshape(1, N * chirpPerF0 \
* (2 * np.floor(stepNotes / scopeAllowedHF0) \
+ 1))
dim2index = np.outer(np.arange(N),
np.ones(chirpPerF0 \
* (2 * np.floor(stepNotes \
/ scopeAllowedHF0) + 1), \
dtype=int)\
).reshape(1, N * chirpPerF0 \
* (2 * np.floor(stepNotes \
/ scopeAllowedHF0) \
+ 1))
HF00[dim1index, dim2index] = 1 # HF0.max()
HF00[:, indexBestPath == (NF0 - 1)] = 0.0
WF0effective = WF0
HF00effective = HF00
del HF0, HGAMMA, HPHI, HM, WM, HF00
HGAMMA, HPHI, HF0, HM, WM, recoError2 = SIMM.SIMM(
# the data to be fitted to:
SX,
# the basis matrices for the spectral combs
WF0effective,
# and for the elementary filters:
WGAMMA,
# number of desired filters, accompaniment spectra:
numberOfFilters=K, numberOfAccompanimentSpectralShapes=R,
# if any, initial amplitude matrices for
HGAMMA0=None, HPHI0=None,
HF00=HF00effective,
WM0=None, HM0=None,
# Some more optional arguments, to control the "convergence"
# of the algo
numberOfIterations=niter, updateRulePower=1.0,
stepNotes=stepNotes,
lambdaHF0 = 0.0 / (1.0 * SX.max()), alphaHF0=0.9,
verbose=options.verbose, displayEvolution=displayEvolution)
WPHI = np.dot(WGAMMA, HGAMMA)
SPHI = np.dot(WPHI, HPHI)
SF0 = np.dot(WF0effective, HF0)
SM = np.dot(WM, HM)
hatSX = SPHI * SF0 + SM
hatV = SPHI * SF0 / hatSX * X
vest = istft(hatV, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
# scikits.audiolab.wavwrite(vest, options.voc_output_file, fs)
wav.write(options.voc_output_file, fs, \
vest)
hatM = SM / hatSX * X
mest = istft(hatM, hopsize=hopsize, nfft=NFT,
window=sinebell(windowSizeInSamples)) / 4.0
#scikits.audiolab.wavwrite(mest, options.mus_output_file, fs)
wav.write(options.mus_output_file, fs, \
mest)
if displayEvolution:
plt.figure(13);plt.clf()
plt.subplot(221)
plt.plot(db(np.dot(WGAMMA, HGAMMA[:, 0])))
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.ylim([-30, 0])
plt.axis("tight")
plt.subplot(222)
plt.plot(db(np.dot(WGAMMA, HGAMMA[:, 1])))
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.ylim([-30, 0])
plt.axis("tight")
plt.subplot(223)
plt.plot(db(np.dot(WGAMMA, HGAMMA[:, 2])))
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.ylim([-30, 0])
plt.axis("tight")
plt.subplot(224)
plt.plot(db(np.dot(WGAMMA, HGAMMA[:, 3])))
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.ylim([-30, 0])
plt.axis("tight")
plt.figure(14);plt.clf()
imageMatlab.imageM(db(np.dot(np.dot(WGAMMA, HGAMMA), HPHI)))
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel(r'Frame number $n$', fontsize=16)
plt.ylabel(r'Frequency bin number $f$', fontsize=16)
cb = plt.colorbar(fraction=0.04)
plt.axes(cb.ax)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.figure(141);plt.clf()
SVhat = db(np.dot(np.dot(WGAMMA, HGAMMA), HPHI)) \
+ db(np.dot(WF0, HF0))
imageMatlab.imageM(SVhat, vmax=SVhat.max(),
vmin=SVhat.max() - 50)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel(r'Frame number $n$', fontsize=16)
plt.ylabel(r'Frequency bin number $f$', fontsize=16)
cb = plt.colorbar(fraction=0.04)
plt.axes(cb.ax)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.figure(15);plt.clf()
imageMatlab.imageM(db(HF0), vmin=-100, cmap=plt.cm.gray_r)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel(r'Frame number $n$', fontsize=16)
plt.ylabel(r'Leading source number $u$', fontsize=16)
cb = plt.colorbar(fraction=0.04)
plt.axes(cb.ax)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
# plt.xlim([3199.5, 3500.5]) # For detailed picture of HF0
plt.figure(16)
plt.clf()
imageMatlab.imageM(db(np.dot(WM, HM)),
vmin=np.maximum(-50, db(SM.min())))
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel(r'Frame number $n$', fontsize=16)
plt.ylabel(r'Frequency bin number $f$', fontsize=16)
cb = plt.colorbar(fraction=0.04)
plt.axes(cb.ax)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.figure(17)
plt.clf()
imageMatlab.imageM(db(WM), vmin=-50)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel(r'Element number $r$', fontsize=16)
plt.ylabel(r'Frequency bin number $f$', fontsize=16)
cb = plt.colorbar(fraction=0.04)
plt.axes(cb.ax)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
raw_input("Press Return to end the program...")
print "Done!"