def visualize(N, mX, pin, fs, ploc, iploc, ipmag, ipphase):
'''
visuaizes
'''
ipfreq = fs * iploc / N
# generate total spectrum by main lobe
X = UF.genSpecSines_p(ipfreq, ipmag, ipphase, N, fs) # generate spec sines
hN = N / 2 + 1 # size of positive spectrum
absX = abs(X[:hN]) # compute ansolute value of positive side
absX[absX < np.finfo(float).eps] = np.finfo(
float).eps # if zeros add epsilon to handle log
generatedX = absX
# generatedX = 20 * np.log10(absX)
########################
visualizeSpectum(mX, pin / fs, ploc, iploc, ipmag, generatedX)
def sineModelSynth(tfreq, tmag, tphase, N, H, fs):
"""
Synthesis of a sound using the sinusoidal model
tfreq,tmag,tphase: frequencies, magnitudes and phases of sinusoids
N: synthesis FFT size, H: hop size, fs: sampling rate
returns y: output array sound
"""
hN = N // 2 # half of FFT size for synthesis
L = tfreq.shape[0] # number of frames
pout = 0 # initialize output sound pointer
ysize = H * (L + 3) # output sound size
y = np.zeros(ysize) # initialize output array
sw = np.zeros(N) # initialize synthesis window
ow = triang(2 * H) # triangular window
sw[hN - H:hN + H] = ow # add triangular window
bh = blackmanharris(N) # blackmanharris window
bh = bh / sum(bh) # normalized blackmanharris window
sw[hN - H:hN +
H] = sw[hN - H:hN + H] / bh[hN - H:hN +
H] # normalized synthesis window
lastytfreq = tfreq[0, :] # initialize synthesis frequencies
ytphase = 2 * np.pi * np.random.rand(
tfreq[0, :].size) # initialize synthesis phases
for l in range(L): # iterate over all frames
if (tphase.size > 0): # if no phases generate them
ytphase = tphase[l, :]
else:
ytphase += (np.pi * (lastytfreq + tfreq[l, :]) /
fs) * H # propagate phases
Y = UF.genSpecSines_p(tfreq[l, :], tmag[l, :], ytphase, N,
fs) # generate sines in the spectrum
lastytfreq = tfreq[l, :] # save frequency for phase propagation
ytphase = ytphase % (2 * np.pi) # make phase inside 2*pi
yw = np.real(fftshift(ifft(Y))) # compute inverse FFT
y[pout:pout + N] += sw * yw # overlap-add and apply a synthesis window
pout += H # advance sound pointer
y = np.delete(y, range(hN)) # delete half of first window
y = np.delete(y, range(y.size - hN,
y.size)) # delete half of the last window
return y
from scipy.fftpack import ifft
import dftModel as DFT
(fs, x) = UF.wavread('../../sounds/oboe-A4.wav')
Ns = 512
hNs = Ns / 2
H = Ns / 4
M = 511
t = -70
w = get_window('hamming', M)
x1 = x[.8*fs:0.8*fs+M]
mX, pX = DFT.dftAnal(x1, w, Ns)
ploc = UF.peakDetection(mX, t)
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc)
ipfreq = fs * iploc / float(Ns)
Y = UF.genSpecSines_p(ipfreq, ipmag, ipphase, Ns, fs)
y = np.real(ifft(Y))
sw = np.zeros(Ns)
ow = triang(Ns/2)
sw[hNs-H:hNs+H] = ow
bh = blackmanharris(Ns)
bh = bh / sum(bh)
sw[hNs-H:hNs+H] = sw[hNs-H:hNs+H] / bh[hNs-H:hNs+H]
yw = np.zeros(Ns)
yw[:hNs-1] = y[hNs+1:]
yw[hNs-1:] = y[:hNs+1]
yw *= sw
freqaxis = fs * np.arange(Ns/2 + 1) / float(Ns)
from scipy.signal import blackmanharris, triang
from scipy.fftpack import ifft
(fs, x) = UF.wavread('../../sounds/oboe-A4.wav')
Ns = 512
hNs = Ns / 2
H = Ns / 4
M = 511
t = -70
w = get_window('hamming', M)
x1 = x[.8 * fs:.8 * fs + M]
mX, pX = DFT.dftAnal(x1, w, Ns)
ploc = UF.peakDetection(mX, t)
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc)
ipfreq = fs * iploc / float(Ns)
Y = UF.genSpecSines_p(ipfreq, ipmag, ipphase, Ns, fs)
y = np.real(ifft(Y))
sw = np.zeros(Ns)
ow = triang(Ns / 2)
sw[hNs - H:hNs + H] = ow
bh = blackmanharris(Ns)
bh = bh / sum(bh)
sw[hNs - H:hNs + H] = sw[hNs - H:hNs + H] / bh[hNs - H:hNs + H]
yw = np.zeros(Ns)
yw[:hNs - 1] = y[hNs + 1:]
yw[hNs - 1:] = y[:hNs + 1]
yw *= sw
freqaxis = fs * np.arange(Ns / 2 + 1) / float(Ns)
def harmonicModel(x, fs, w, N, t, nH, minf0, maxf0, f0et):
"""
Analysis/synthesis of a sound using the sinusoidal harmonic model
x: input sound, fs: sampling rate, w: analysis window,
N: FFT size (minimum 512), 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),
returns y: output array sound
"""
hN = N // 2 # size of positive spectrum
hM1 = int(math.floor(
(w.size + 1) / 2)) # half analysis window size by rounding
hM2 = int(math.floor(w.size / 2)) # half analysis window size by floor
x = np.append(
np.zeros(hM2),
x) # add zeros at beginning to center first window at sample 0
x = np.append(x,
np.zeros(hM1)) # add zeros at the end to analyze last sample
Ns = 512 # FFT size for synthesis (even)
H = Ns / 4 # Hop size used for analysis and synthesis
hNs = Ns / 2
pin = max(hNs, hM1) # init sound pointer in middle of anal window
pend = x.size - max(hNs, hM1) # last sample to start a frame
fftbuffer = np.zeros(N) # initialize buffer for FFT
yh = np.zeros(Ns) # initialize output sound frame
y = np.zeros(x.size) # initialize output array
w = w / sum(w) # normalize analysis window
sw = np.zeros(Ns) # initialize synthesis window
ow = triang(2 * H) # overlapping window
sw[hNs - H:hNs + H] = ow
bh = blackmanharris(Ns) # synthesis window
bh = bh / sum(bh) # normalize synthesis window
sw[hNs - H:hNs +
H] = sw[hNs - H:hNs + H] / bh[hNs - H:hNs + H] # window for overlap-add
hfreqp = []
f0t = 0
f0stable = 0
while pin < pend:
#-----analysis-----
x1 = x[pin - hM1:pin + hM2] # select frame
mX, pX = DFT.dftAnal(x1, w, N) # compute dft
ploc = UF.peakDetection(mX, t) # detect peak locations
iploc, ipmag, ipphase = UF.peakInterp(mX, pX,
ploc) # refine peak values
ipfreq = fs * iploc / N
f0t = UF.f0Twm(ipfreq, ipmag, f0et, minf0, maxf0, f0stable) # find f0
if ((f0stable==0)&(f0t>0)) \
or ((f0stable>0)&(np.abs(f0stable-f0t)<f0stable/5.0)):
f0stable = f0t # consider a stable f0 if it is close to the previous one
else:
f0stable = 0
hfreq, hmag, hphase = harmonicDetection(ipfreq, ipmag, ipphase, f0t,
nH, hfreqp,
fs) # find harmonics
hfreqp = hfreq
#-----synthesis-----
Yh = UF.genSpecSines_p(hfreq, hmag, hphase, Ns,
fs) # generate spec sines
fftbuffer = np.real(ifft(Yh)) # inverse FFT
yh[:hNs - 1] = fftbuffer[hNs + 1:] # undo zero-phase window
yh[hNs - 1:] = fftbuffer[:hNs + 1]
y[pin - hNs:pin + hNs] += sw * yh # overlap-add
pin += H # advance sound pointer
y = np.delete(
y,
range(hM2)) # delete half of first window which was added in stftAnal
y = np.delete(y,
range(y.size - hM1,
y.size)) # add zeros at the end to analyze last sample
return y
Ns = 512
hNs = int(Ns/2)
H = int(Ns/4)
t = -70 #min threshold from the peak for other peaks
w = get_window('hamming', M)
x1 = x[int(.8*fs):int(.8*fs) + M]
mX, pX = DFT.dftAnal(x1, w, Ns)
peaks = UF.peakDetection(mX, t)
ipeaks, iMag, iPhase = UF.peakInterp(mX, pX, peaks)
ipfreq = fs * ipeaks / float(Ns)
#ipfreq = np.array([3000.0, 4000.0])
#iMag = np.array([0.0, 0.0])
#iPhase = np.array([0.0, 0.0])
Y = UF.genSpecSines_p(ipfreq, iMag, iPhase, Ns, fs)
y = np.real(ifft(Y))
print(y.size)
sw = np.zeros(Ns) #code in order to undo the blackman harris window and apply triangular function
ow = triang(Ns/2)
sw[hNs-H:hNs + H] = ow
bh = blackmanharris(Ns)
bh = bh / sum(bh)
sw[hNs-H:hNs + H] = sw[hNs-H:hNs+H] / bh[hNs-H:hNs+H]
yw = np.zeros(Ns)
yw[:hNs-1] = y[hNs+1:]
yw[hNs-1:] = y[:hNs+1]
yw *= sw
(fs, x) = UF.wavread('../../sounds/oboe-A4.wav')
Ns = 512
hNs = Ns / 2
H = Ns / 4
M = 511
t = -70
w = get_window('hamming', M)
x1 = x[int(.8 * fs):int(.8 * fs) + M]
mX, pX = DFT.dftAnal(x1, w, Ns)
ploc = UF.peakDetection(mX, t)
iploc, ipmag, ipphase = UF.peakInterp(
mX, pX, ploc
) #interpolate the peaks to get more accurate value of the location of the sinusoid
ipfreq = fs * iploc / float(Ns)
Y = UF.genSpecSines_p(ipfreq, ipmag, ipphase, Ns, fs) # do the synthesis
y = np.real(ifft(Y)) # inverse fft
# undo the window
sw = np.zeros(Ns)
ow = triang(Ns / 2)
sw[int(hNs - H):int(hNs + H)] = ow
bh = blackmanharris(Ns)
bh = bh / sum(bh)
sw[int(hNs -
H):int(hNs +
H)] = sw[int(hNs - H):int(hNs + H)] / bh[int(hNs - H):int(hNs +
H)]
yw = np.zeros(Ns)
yw[:int(hNs - 1)] = y[int(hNs + 1):]
sys.path.append(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
"/home/pvardanis/sms-tools/software/models/"))
import dftModel as DFT
import utilFunctions as UF
from scipy.fftpack import ifft
from scipy.signal import blackmanharris, triang
fs = 44100
Ns = 512
hNs = int(Ns / 2)
H = int(Ns / 4)
ipfreq = np.array([4000.0]) # frequency of the sinusoid
ipmag = np.array([0.0])
ipphase = np.array([0.0])
Y = UF.genSpecSines_p(
ipfreq, ipmag, ipphase, Ns,
fs) # create the frequency spectrum of a blackmanharris window
y = np.real(ifft(Y))
sw = np.zeros(Ns) # create a triangular window for better hopping factor
ow = triang(Ns / 2)
sw[hNs - H:hNs + H] = ow # center around the middle
bh = blackmanharris(Ns)
bh = bh / sum(bh)
sw[hNs - H:hNs + H] = sw[hNs - H:hNs + H] / bh[hNs - H:hNs + H]
yw = np.zeros(Ns)
yw[:hNs - 1] = y[hNs + 1:]
yw[hNs - 1:] = y[:hNs + 1]
yw *= sw
