def hprModelAnal(x, fs, w, N, H, t, minSineDur, nH, minf0, maxf0, f0et, harmDevSlope): """Analysis of a sound using the harmonic plus residual model x: input sound, fs: sampling rate, w: analysis window; N: FFT size, t: threshold in negative dB, minSineDur: minimum duration of sinusoidal tracks nH: maximum number of harmonics; minf0: minimum fundamental frequency in sound maxf0: maximum fundamental frequency in sound; f0et: maximum error accepted in f0 detection algorithm harmDevSlope: allowed deviation of harmonic tracks, higher harmonics have higher allowed deviation returns hfreq, hmag, hphase: harmonic frequencies, magnitude and phases; xr: residual signal """ # perform harmonic analysis hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur) Ns = 512 xr = UF.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs) # subtract sinusoids from original sound return hfreq, hmag, hphase, xr
def hprModelAnal(x, fs, w, N, H, t, minSineDur, nH, minf0, maxf0, f0et, harmDevSlope): """Analysis of a sound using the harmonic plus residual model x: input sound, fs: sampling rate, w: analysis window; N: FFT size, t: threshold in negative dB, minSineDur: minimum duration of sinusoidal tracks nH: maximum number of harmonics; minf0: minimum fundamental frequency in sound maxf0: maximum fundamental frequency in sound; f0et: maximum error accepted in f0 detection algorithm harmDevSlope: allowed deviation of harmonic tracks, higher harmonics have higher allowed deviation returns hfreq, hmag, hphase: harmonic frequencies, magnitude and phases; xr: residual signal """ # perform harmonic analysis hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur) Ns = 512 xr = UF.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs) # subtract sinusoids from original sound return hfreq, hmag, hphase, xr
def sprModelAnal(x, fs, w, N, H, t, minSineDur, maxnSines, freqDevOffset, freqDevSlope): """ Analysis of a sound using the sinusoidal plus residual model x: input sound, fs: sampling rate, w: analysis window; N: FFT size, t: threshold in negative dB, minSineDur: minimum duration of sinusoidal tracks maxnSines: maximum number of parallel sinusoids freqDevOffset: frequency deviation allowed in the sinusoids from frame to frame at frequency 0 freqDevSlope: slope of the frequency deviation, higher frequencies have bigger deviation returns hfreq, hmag, hphase: harmonic frequencies, magnitude and phases; xr: residual signal """ # perform sinusoidal analysis tfreq, tmag, tphase = SM.sineModelAnal(x, fs, w, N, H, t, maxnSines, minSineDur, freqDevOffset, freqDevSlope) Ns = 512 xr = UF.sineSubtraction(x, Ns, H, tfreq, tmag, tphase, fs) # subtract sinusoids from original sound return tfreq, tmag, tphase, xr
def hpsModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur, Ns, stocf): """ Analysis of a sound using the harmonic plus stochastic model x: input sound, fs: sampling rate, w: analysis window; N: FFT size, t: threshold in negative dB, nH: maximum number of harmonics, minf0: minimum f0 frequency in Hz, maxf0: maximim f0 frequency in Hz; f0et: error threshold in the f0 detection (ex: 5), harmDevSlope: slope of harmonic deviation; minSineDur: minimum length of harmonics returns hfreq, hmag, hphase: harmonic frequencies, magnitude and phases; stocEnv: stochastic residual """ # perform harmonic analysis hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur) # subtract sinusoids from original sound xr = UF.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs) # perform stochastic analysis of residual stocEnv = STM.stochasticModelAnal(xr, H, H*2, stocf) return hfreq, hmag, hphase, stocEnv
def hpsModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur, Ns, stocf): """ Analysis of a sound using the harmonic plus stochastic model x: input sound, fs: sampling rate, w: analysis window; N: FFT size, t: threshold in negative dB, nH: maximum number of harmonics, minf0: minimum f0 frequency in Hz, maxf0: maximim f0 frequency in Hz; f0et: error threshold in the f0 detection (ex: 5), harmDevSlope: slope of harmonic deviation; minSineDur: minimum length of harmonics returns hfreq, hmag, hphase: harmonic frequencies, magnitude and phases; stocEnv: stochastic residual """ # perform harmonic analysis hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur) # subtract sinusoids from original sound xr = UF.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs) # perform stochastic analysis of residual stocEnv = STM.stochasticModelAnal(xr, H, H*2, stocf) return hfreq, hmag, hphase, stocEnv
def spsModelAnal(x, fs, w, N, H, t, minSineDur, maxnSines, freqDevOffset, freqDevSlope, stocf): """ Analysis of a sound using the sinusoidal plus stochastic model x: input sound, fs: sampling rate, w: analysis window; N: FFT size, t: threshold in negative dB, minSineDur: minimum duration of sinusoidal tracks maxnSines: maximum number of parallel sinusoids freqDevOffset: frequency deviation allowed in the sinusoids from frame to frame at frequency 0 freqDevSlope: slope of the frequency deviation, higher frequencies have bigger deviation stocf: decimation factor used for the stochastic approximation returns hfreq, hmag, hphase: harmonic frequencies, magnitude and phases; stocEnv: stochastic residual """ # perform sinusoidal analysis tfreq, tmag, tphase = SM.sineModelAnal(x, fs, w, N, H, t, maxnSines, minSineDur, freqDevOffset, freqDevSlope) Ns = 512 xr = UF.sineSubtraction(x, Ns, H, tfreq, tmag, tphase, fs) # subtract sinusoids from original sound stocEnv = STM.stochasticModelAnal(xr, H, H*2, stocf) # compute stochastic model of residual return tfreq, tmag, tphase, stocEnv
def spsModelAnal(x, fs, w, N, H, t, minSineDur, maxnSines, freqDevOffset,
freqDevSlope, stocf):
"""
Analysis of a sound using the sinusoidal plus stochastic model
x: input sound, fs: sampling rate, w: analysis window; N: FFT size, t: threshold in negative dB,
minSineDur: minimum duration of sinusoidal tracks
maxnSines: maximum number of parallel sinusoids
freqDevOffset: frequency deviation allowed in the sinusoids from frame to frame at frequency 0
freqDevSlope: slope of the frequency deviation, higher frequencies have bigger deviation
stocf: decimation factor used for the stochastic approximation
returns hfreq, hmag, hphase: harmonic frequencies, magnitude and phases; stocEnv: stochastic residual
"""
# perform sinusoidal analysis
tfreq, tmag, tphase = SM.sineModelAnal(x, fs, w, N, H, t, maxnSines,
minSineDur, freqDevOffset,
freqDevSlope)
Ns = N #512
xr = UF.sineSubtraction(x, Ns, H, tfreq, tmag, tphase,
fs) # subtract sinusoids from original sound
#stocEnv = STM.stochasticModelAnal(xr, H, H*2, stocf) # compute stochastic model of residual
stocEnv = STM.stochasticModelAnal(xr, H, N, stocf)
return tfreq, tmag, tphase, stocEnv
import numpy as np import matplotlib.pyplot as plt from scipy.signal import hamming, hanning, triang, blackmanharris, resample import math import sys, os, time sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../software/models/')) import stft as STFT import utilFunctions as UF import harmonicModel as HM (fs, x) = UF.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../sounds/cello-double.wav')) w = np.blackman(3501) N = 2048*2 t = -100 nH = 100 minf0 = 140 maxf0 = 150 f0et = 10 minSineDur = .2 harmDevSlope = 0.001 Ns = 512 H = Ns/4 hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur) y = HM.harmonicModelSynth(hfreq, hmag, hphase, Ns, H, fs) xr = UF.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs)
return y, ys, xr # test the subtraction of sines if __name__ == '__main__': (fs, x) = UF.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/bendir.wav')) w = np.hamming(2001) N = 2048 H = 128 t = -100 minSineDur = .02 maxnSines = 200 freqDevOffset = 10 freqDevSlope = 0.001 tfreq, tmag, tphase = SM.sineModelAnal(x, fs, w, N, H, t, maxnSines, minSineDur, freqDevOffset, freqDevSlope) xr = UF.sineSubtraction(x, N, H, tfreq, tmag, tphase, fs) mXr, pXr = STFT.stftAnal(xr, fs, hamming(H*2), H*2, H) Ns = 512 ys = SM.sineModelSynth(tfreq, tmag, tphase, Ns, H, fs) plt.figure(1, figsize=(9.5, 7)) numFrames = int(mXr[:,0].size) frmTime = H*np.arange(numFrames)/float(fs) binFreq = np.arange(H)*float(fs)/(H*2) plt.pcolormesh(frmTime, binFreq, np.transpose(mXr)) plt.autoscale(tight=True) tfreq[tfreq==0] = np.nan numFrames = int(tfreq[:,0].size) frmTime = H*np.arange(numFrames)/float(fs) plt.plot(frmTime, tfreq, color='k', ms=3, alpha=1)
def main(inputFile='../../sounds/bendir.wav', window='hamming', M=2001, N=2048, t=-80,
minSineDur=0.02, maxnSines=150, freqDevOffset=10, freqDevSlope=0.001):
# ------- analysis parameters -------------------
# inputFile: input sound file (monophonic with sampling rate of 44100)
# window: analysis window type (rectangular, hanning, hamming, blackman, blackmanharris)
# M: analysis window size
# N: fft size (power of two, bigger or equal than M)
# t: magnitude threshold of spectral peaks
# minSineDur: minimum duration of sinusoidal tracks
# maxnSines: maximum number of parallel sinusoids
# freqDevOffset: frequency deviation allowed in the sinusoids from frame to frame at frequency 0
# freqDevSlope: slope of the frequency deviation, higher frequencies have bigger deviation
# size of fft used in synthesis
Ns = 512
# hop size (has to be 1/4 of Ns)
H = 128
# --------- computation -----------------
# read input sound
(fs, x) = UF.wavread(inputFile)
# compute analysis window
w = get_window(window, M)
# perform sinusoidal analysis
tfreq, tmag, tphase = SM.sineModelAnal(x, fs, w, N, H, t, maxnSines, minSineDur, freqDevOffset, freqDevSlope)
# subtract sinusoids from original
xr = UF.sineSubtraction(x, N, H, tfreq, tmag, tphase, fs)
# compute spectrogram of residual
mXr, pXr = STFT.stftAnal(xr, fs, w, N, H)
# synthesize sinusoids
ys = SM.sineModelSynth(tfreq, tmag, tphase, Ns, H, fs)
# sum sinusoids and residual
y = xr[:min(xr.size, ys.size)]+ys[:min(xr.size, ys.size)]
# output sound file (monophonic with sampling rate of 44100)
outputFileSines = 'output_sounds/' + os.path.basename(inputFile)[:-4] + '_sprModel_sines.wav'
outputFileResidual = 'output_sounds/' + os.path.basename(inputFile)[:-4] + '_sprModel_residual.wav'
outputFile = 'output_sounds/' + os.path.basename(inputFile)[:-4] + '_sprModel.wav'
# write sounds files for sinusoidal, residual, and the sum
UF.wavwrite(ys, fs, outputFileSines)
UF.wavwrite(xr, fs, outputFileResidual)
UF.wavwrite(y, fs, outputFile)
# --------- plotting --------------------
# create figure to show plots
plt.figure(figsize=(12, 9))
# frequency range to plot
maxplotfreq = 5000.0
# plot the input sound
plt.subplot(3,1,1)
plt.plot(np.arange(x.size)/float(fs), x)
plt.axis([0, x.size/float(fs), min(x), max(x)])
plt.ylabel('amplitude')
plt.xlabel('time (sec)')
plt.title('input sound: x')
# plot the magnitude spectrogram of residual
plt.subplot(3,1,2)
maxplotbin = int(N*maxplotfreq/fs)
numFrames = int(mXr[:,0].size)
frmTime = H*np.arange(numFrames)/float(fs)
binFreq = np.arange(maxplotbin+1)*float(fs)/N
plt.pcolormesh(frmTime, binFreq, np.transpose(mXr[:,:maxplotbin+1]))
plt.autoscale(tight=True)
# plot the sinusoidal frequencies on top of the residual spectrogram
tracks = tfreq*np.less(tfreq, maxplotfreq)
tracks[tracks<=0] = np.nan
plt.plot(frmTime, tracks, color='k')
plt.title('sinusoidal tracks + residual spectrogram')
plt.autoscale(tight=True)
# plot the output sound
plt.subplot(3,1,3)
plt.plot(np.arange(y.size)/float(fs), y)
plt.axis([0, y.size/float(fs), min(y), max(y)])
plt.ylabel('amplitude')
plt.xlabel('time (sec)')
plt.title('output sound: y')
plt.tight_layout()
plt.show()
def main(
inputFile="../../sounds/sax-phrase.wav",
window="blackman",
M=601,
N=1024,
t=-100,
minSineDur=0.1,
nH=100,
minf0=350,
maxf0=700,
f0et=5,
harmDevSlope=0.01,
):
# ------- analysis parameters -------------------
# inputFile: input sound file (monophonic with sampling rate of 44100)
# window: analysis window type (rectangular, hanning, hamming, blackman, blackmanharris)
# M: analysis window size
# N: fft size (power of two, bigger or equal than M)
# t: magnitude threshold of spectral peaks
# minSineDur: minimum duration of sinusoidal tracks
# nH: maximum number of harmonics
# minf0: minimum fundamental frequency in sound
# maxf0: maximum fundamental frequency in sound
# f0et: maximum error accepted in f0 detection algorithm
# harmDevSlope: allowed deviation of harmonic tracks, higher harmonics have higher allowed deviation
# size of fft used in synthesis
Ns = 512
# hop size (has to be 1/4 of Ns)
H = 128
# --------- computation -----------------
# read input sound
(fs, x) = UF.wavread(inputFile)
# compute analysis window
w = get_window(window, M)
# find harmonics
hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)
# subtract harmonics from original sound
xr = UF.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs)
# compute spectrogram of residual
mXr, pXr = STFT.stftAnal(xr, fs, w, N, H)
# synthesize harmonic component
yh = SM.sineModelSynth(hfreq, hmag, hphase, Ns, H, fs)
# sum harmonics and residual
y = xr[: min(xr.size, yh.size)] + yh[: min(xr.size, yh.size)]
# output sound file (monophonic with sampling rate of 44100)
outputFileSines = "output_sounds/" + os.path.basename(inputFile)[:-4] + "_hprModel_sines.wav"
outputFileResidual = "output_sounds/" + os.path.basename(inputFile)[:-4] + "_hprModel_residual.wav"
outputFile = "output_sounds/" + os.path.basename(inputFile)[:-4] + "_hprModel.wav"
# write sounds files for harmonics, residual, and the sum
UF.wavwrite(yh, fs, outputFileSines)
UF.wavwrite(xr, fs, outputFileResidual)
UF.wavwrite(y, fs, outputFile)
# --------- plotting --------------------
# create figure to plot
plt.figure(figsize=(12, 9))
# frequency range to plot
maxplotfreq = 5000.0
# plot the input sound
plt.subplot(3, 1, 1)
plt.plot(np.arange(x.size) / float(fs), x)
plt.axis([0, x.size / float(fs), min(x), max(x)])
plt.ylabel("amplitude")
plt.xlabel("time (sec)")
plt.title("input sound: x")
# plot the magnitude spectrogram of residual
plt.subplot(3, 1, 2)
maxplotbin = int(N * maxplotfreq / fs)
numFrames = int(mXr[:, 0].size)
frmTime = H * np.arange(numFrames) / float(fs)
binFreq = np.arange(maxplotbin + 1) * float(fs) / N
plt.pcolormesh(frmTime, binFreq, np.transpose(mXr[:, : maxplotbin + 1]))
plt.autoscale(tight=True)
# plot harmonic frequencies on residual spectrogram
harms = hfreq * np.less(hfreq, maxplotfreq)
harms[harms == 0] = np.nan
numFrames = int(harms[:, 0].size)
frmTime = H * np.arange(numFrames) / float(fs)
plt.plot(frmTime, harms, color="k", ms=3, alpha=1)
plt.xlabel("time(s)")
plt.ylabel("frequency(Hz)")
plt.autoscale(tight=True)
plt.title("harmonics + residual spectrogram")
# plot the output sound
plt.subplot(3, 1, 3)
plt.plot(np.arange(y.size) / float(fs), y)
plt.axis([0, y.size / float(fs), min(y), max(y)])
plt.ylabel("amplitude")
plt.xlabel("time (sec)")
plt.title("output sound: y")
plt.tight_layout()
plt.show()
def main(
inputFile="../../sounds/bendir.wav",
window="hamming",
M=2001,
N=2048,
t=-80,
minSineDur=0.02,
maxnSines=150,
freqDevOffset=10,
freqDevSlope=0.001,
stocf=0.2,
):
# ------- analysis parameters -------------------
# inputFile: input sound file (monophonic with sampling rate of 44100)
# window: analysis window type (rectangular, hanning, hamming, blackman, blackmanharris)
# M: analysis window size
# N: fft size (power of two, bigger or equal than M)
# t: magnitude threshold of spectral peaks
# minSineDur: minimum duration of sinusoidal tracks
# maxnSines: maximum number of parallel sinusoids
# freqDevOffset: frequency deviation allowed in the sinusoids from frame to frame at frequency 0
# freqDevSlope: slope of the frequency deviation, higher frequencies have bigger deviation
# stocf: decimation factor used for the stochastic approximation
# size of fft used in synthesis
Ns = 512
# hop size (has to be 1/4 of Ns)
H = 128
# --------- computation -----------------
# read input sound
(fs, x) = UF.wavread(inputFile)
# compute analysis window
w = get_window(window, M)
# perform sinusoidal analysis
tfreq, tmag, tphase = SM.sineModelAnal(x, fs, w, N, H, t, maxnSines, minSineDur, freqDevOffset, freqDevSlope)
# subtract sinusoids from original sound
Ns = 512
xr = UF.sineSubtraction(x, Ns, H, tfreq, tmag, tphase, fs)
# compute stochastic model of residual
mYst = STM.stochasticModelAnal(xr, H, stocf)
# synthesize sinusoids
ys = SM.sineModelSynth(tfreq, tmag, tphase, Ns, H, fs)
# synthesize stochastic component
yst = STM.stochasticModelSynth(mYst, H)
# sum sinusoids and stochastic
y = yst[: min(yst.size, ys.size)] + ys[: min(yst.size, ys.size)]
# output sound file (monophonic with sampling rate of 44100)
outputFileSines = "output_sounds/" + os.path.basename(inputFile)[:-4] + "_spsModel_sines.wav"
outputFileStochastic = "output_sounds/" + os.path.basename(inputFile)[:-4] + "_spsModel_stochastic.wav"
outputFile = "output_sounds/" + os.path.basename(inputFile)[:-4] + "_spsModel.wav"
# write sounds files for sinusoidal, residual, and the sum
UF.wavwrite(ys, fs, outputFileSines)
UF.wavwrite(yst, fs, outputFileStochastic)
UF.wavwrite(y, fs, outputFile)
# --------- plotting --------------------
# plot stochastic component
plt.figure(figsize=(12, 9))
# frequency range to plot
maxplotfreq = 10000.0
# plot the input sound
plt.subplot(3, 1, 1)
plt.plot(np.arange(x.size) / float(fs), x)
plt.axis([0, x.size / float(fs), min(x), max(x)])
plt.ylabel("amplitude")
plt.xlabel("time (sec)")
plt.title("input sound: x")
plt.subplot(3, 1, 2)
numFrames = int(mYst[:, 0].size)
sizeEnv = int(mYst[0, :].size)
frmTime = H * np.arange(numFrames) / float(fs)
binFreq = (0.5 * fs) * np.arange(sizeEnv * maxplotfreq / (0.5 * fs)) / sizeEnv
plt.pcolormesh(frmTime, binFreq, np.transpose(mYst[:, : sizeEnv * maxplotfreq / (0.5 * fs) + 1]))
plt.autoscale(tight=True)
# plot sinusoidal frequencies on top of stochastic component
sines = tfreq * np.less(tfreq, maxplotfreq)
sines[sines == 0] = np.nan
numFrames = int(sines[:, 0].size)
frmTime = H * np.arange(numFrames) / float(fs)
plt.plot(frmTime, sines, color="k", ms=3, alpha=1)
plt.xlabel("time(s)")
plt.ylabel("Frequency(Hz)")
plt.autoscale(tight=True)
plt.title("sinusoidal + stochastic spectrogram")
# plot the output sound
plt.subplot(3, 1, 3)
plt.plot(np.arange(y.size) / float(fs), y)
plt.axis([0, y.size / float(fs), min(y), max(y)])
plt.ylabel("amplitude")
plt.xlabel("time (sec)")
plt.title("output sound: y")
plt.tight_layout()
plt.show()
N = 1024
t = -100
nH = 40
minf0 = 420
maxf0 = 460
f0et = 5
maxnpeaksTwm = 5
minSineDur = .1
harmDevSlope = 0.01
Ns = 512
H = Ns / 4
mX, pX = STFT.stftAnal(x, fs, w, N, H)
hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0,
f0et, harmDevSlope, minSineDur)
xr = UF.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs)
mXr, pXr = STFT.stftAnal(xr, fs, hamming(Ns), Ns, H)
maxplotfreq = 5000.0
plt.figure(1, figsize=(9, 7))
plt.subplot(221)
numFrames = int(mX[:, 0].size)
frmTime = H * np.arange(numFrames) / float(fs)
binFreq = fs * np.arange(N * maxplotfreq / fs) / N
plt.pcolormesh(frmTime, binFreq,
np.transpose(mX[:, :N * maxplotfreq / fs + 1]))
plt.autoscale(tight=True)
harms = hfreq * np.less(hfreq, maxplotfreq)
harms[harms == 0] = np.nan
