def stftAnal(x, win, N, H):
if (H <= 0):
raise ValueError("Hop size too small!")
M = win.size
pM1 = (M + 1) // 2
pM2 = M // 2
x = np.append(np.zeros(pM2), x)
x = np.append(x, np.zeros(pM2))
begin = pM1 # init sound pointer in the middle of analysis window
end = x.size - pM1
win = win / sum(win)
xmX = []
xphX = []
while begin <= end:
x1 = x[begin - pM1:begin + pM2]
mX, phX = dft.dftAnal(x1, win, N)
xmX.append(np.array(mX))
xphX.append(np.array(phX))
begin += H
xmX = np.array(xmX)
xphX = np.array(xphX)
return xmX, xphX
def harmonicModel(x, fs, w, N, t, nH, minf0, maxf0, f0et): hM1 = int(math.floor((w.size+1)/2)) hM2 = int(math.floor(w.size/2)) x = np.append(np.zeros(hM2),x) x = np.append(x,np.zeros(hM1)) Ns = 512 H = int(Ns/4) hNs = int(Ns/2) begin = max(hNs, hM1) end = x.size - max(hNs, hM1) fftbuffer = np.zeros(N) yh = np.zeros(Ns) y = np.zeros(x.size) w = w / sum(w) sw = np.zeros(Ns) ow = triang(2*H) 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] hfreqp = [] f0t = 0 f0stable = 0 while begin<end: # analyze x1 = x[begin-hM1:begin+hM2] mX, pX = DFT.dftAnal(x1, w, N) ploc = U.peakDetection(mX, t) iploc, ipmag, ipphase = U.peak_parabolicInterp(mX, pX, ploc) ipfreq = fs * iploc/N f0t = U.f0Twm(ipfreq, ipmag, f0et, minf0, maxf0, f0stable) if ((f0stable==0)&(f0t>0)) \ or ((f0stable>0)&(np.abs(f0stable-f0t)<f0stable/5.0)): f0stable = f0t else: f0stable = 0 hfreq, hmag, hphase = harmonicDetection(ipfreq, ipmag, ipphase, f0t, nH, hfreqp, fs) hfreqp = hfreq # synth Yh = U.genSinesSpectrum(hfreq, hmag, hphase, Ns, fs) fftbuffer = np.real(ifft(Yh)) yh[:hNs-1] = fftbuffer[hNs+1:] yh[hNs-1:] = fftbuffer[:hNs+1] y[begin-hNs:begin+hNs] += sw*yh begin += H y = np.delete(y, range(hM2)) y = np.delete(y, range(y.size-hM1, y.size)) return y
def harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope=0.01, minSineDur=.02):
if (minSineDur <0):
raise ValueError("Minimum duration of sine tracks smaller than 0")
hM1 = int(math.floor((w.size+1)/2))
hM2 = int(math.floor(w.size/2))
x = np.append(np.zeros(hM2),x)
x = np.append(x,np.zeros(hM2))
begin = hM1
end = x.size - hM1
w = w / sum(w)
hfreqp = []
f0t = 0
f0stable = 0
while begin<=end:
x1 = x[begin-hM1:begin+hM2]
mX, pX = DFT.dftAnal(x1, w, N)
ploc = U.peakDetection(mX, t)
iploc, ipmag, ipphase = U.peak_parabolicInterp(mX, pX, ploc)
ipfreq = fs * iploc/N
f0t = U.f0Twm(ipfreq, ipmag, f0et, minf0, maxf0, f0stable)
if ((f0stable==0)&(f0t>0)) \
or ((f0stable>0)&(np.abs(f0stable-f0t)<f0stable/5.0)):
f0stable = f0t
else:
f0stable = 0
hfreq, hmag, hphase = harmonicDetection(ipfreq, ipmag, ipphase, f0t, nH, hfreqp, fs, harmDevSlope)
hfreqp = hfreq
if begin == hM1:
xhfreq = np.array([hfreq])
xhmag = np.array([hmag])
xhphase = np.array([hphase])
else:
xhfreq = np.vstack((xhfreq,np.array([hfreq])))
xhmag = np.vstack((xhmag, np.array([hmag])))
xhphase = np.vstack((xhphase, np.array([hphase])))
begin += H
xhfreq = SM.cleanSineTracks(xhfreq, round(fs*minSineDur/H))
return xhfreq, xhmag, xhphase
def sineModelAnal(x, fs, win, N, H, tresh, maxSines = 100, minSineDur=.01, freqDevOffset=20, freqDevSlope=0.01):
if (minSineDur <0):
raise ValueError("Minimum duration is smaller than 0")
hM1 = int(math.floor((win.size+1)/2))
hM2 = int(math.floor(win.size/2))
x = np.append(np.zeros(hM2),x) # center around 0
x = np.append(x,np.zeros(hM2))
begin = hM1
end = x.size - hM1
win = win / sum(win)
tfreq = np.array([])
while begin<end:
x1 = x[begin-hM1:begin+hM2]
mX, pX = dft.dftAnal(x1, win, N)
peaksLocation = U.peakDetection(mX, tresh)
iplocation, ipmag, ipphase = U.peak_parabolicInterp(mX, pX, peaksLocation)
ipfreq = fs*iplocation/float(N)
# perform sinusoidal tracking by adding peaks to trajectories
tfreq, tmag, tphase = sineTracking(ipfreq, ipmag, ipphase, tfreq, freqDevOffset, freqDevSlope)
tfreq = np.resize(tfreq, min(maxSines, tfreq.size))
tmag = np.resize(tmag, min(maxSines, tmag.size))
tphase = np.resize(tphase, min(maxSines, tphase.size))
jtfreq = np.zeros(maxSines)
jtmag = np.zeros(maxSines)
jtphase = np.zeros(maxSines)
jtfreq[:tfreq.size]=tfreq
jtmag[:tmag.size]=tmag
jtphase[:tphase.size]=tphase
if begin == hM1: # if first frame initialize output sine tracks
xtfreq = jtfreq
xtmag = jtmag
xtphase = jtphase
else: # rest of frames append values to sine tracks
xtfreq = np.vstack((xtfreq, jtfreq))
xtmag = np.vstack((xtmag, jtmag))
xtphase = np.vstack((xtphase, jtphase))
begin += H
xtfreq = cleanSineTracks(xtfreq, round(fs*minSineDur/H))
return xtfreq, xtmag, xtphase
def f0Detection(x, fs, w, N, H, t, minf0, maxf0, f0et):
if (minf0 < 0):
raise ValueError("Minumum fundamental frequency (minf0) smaller than 0")
if (maxf0 >= fs/2):
raise ValueError("Maximum fundamental frequency (maxf0) bigger than 10000Hz")
if (H <= 0):
raise ValueError("Hop size (H) smaller or equal to 0")
hM1 = int(math.floor((w.size+1)/2))
hM2 = int(math.floor(w.size/2))
x = np.append(np.zeros(hM2),x)
x = np.append(x,np.zeros(hM1))
begin = hM1
end = x.size - hM1
w = w / sum(w)
f0 = []
f0t = 0
f0stable = 0
while begin<end:
x1 = x[begin-hM1:begin+hM2]
mX, pX = DFT.dftAnal(x1, w, N)
ploc = U.peakDetection(mX, t)
iploc, ipmag, ipphase = U.peak_parabolicInterp(mX, pX, ploc)
ipfreq = fs * iploc/N
f0t = U.f0Twm(ipfreq, ipmag, f0et, minf0, maxf0, f0stable)
if ((f0stable==0)&(f0t>0)) \
or ((f0stable>0)&(np.abs(f0stable-f0t)<f0stable/5.0)):
f0stable = f0t
else:
f0stable = 0
f0 = np.append(f0, f0t)
begin += H
return f0
def hprModel(x, fs, w, N, t, nH, minf0, maxf0, f0et): 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 Ns = 512 # FFT size for synthesis (even) H = Ns//4 # Hop size used for analysis and synthesis hNs = Ns//2 pin = max(hNs, hM1) # initialize sound pointer in middle of analysis window pend = x.size - max(hNs, hM1) # last sample to start a frame fftbuffer = np.zeros(N) # initialize buffer for FFT yhw = np.zeros(Ns) # initialize output sound frame xrw = np.zeros(Ns) # initialize output sound frame yh = np.zeros(x.size) # initialize output array xr = np.zeros(x.size) # initialize output array w = w / sum(w) # normalize analysis window sw = np.zeros(Ns) ow = triang(2*H) sw[hNs-H:hNs+H] = ow bh = blackmanharris(Ns) bh = bh / sum(bh) wr = bh sw[hNs-H:hNs+H] = sw[hNs-H:hNs+H] / bh[hNs-H:hNs+H] hfreqp = [] f0t = 0 f0stable = 0 while pin<pend: # analyze x1 = x[pin-hM1:pin+hM2] mX, pX = DFT.dftAnal(x1, w, N) ploc = U.peakDetection(mX, t) iploc, ipmag, ipphase = U.peak_parabolicInterp(mX, pX, ploc) ipfreq = fs * iploc/N f0t = U.f0Twm(ipfreq, ipmag, f0et, minf0, maxf0, f0stable) if ((f0stable==0)&(f0t>0)) or ((f0stable>0)&(np.abs(f0stable-f0t)<f0stable/5.0)): f0stable = f0t else: f0stable = 0 hfreq, hmag, hphase = HM.harmonicDetection(ipfreq, ipmag, ipphase, f0t, nH, hfreqp, fs) hfreqp = hfreq ri = pin-hNs-1 xw2 = x[ri:ri+Ns]*wr fftbuffer = np.zeros(Ns) fftbuffer[:hNs] = xw2[hNs:] # zero-phase window fftbuffer[hNs:] = xw2[:hNs] X2 = fft(fftbuffer) # synth Yh = U.genSinesSpectrum(hfreq, hmag, hphase, Ns, fs) Xr = X2-Yh fftbuffer = np.zeros(Ns) fftbuffer = np.real(ifft(Yh)) yhw[:hNs-1] = fftbuffer[hNs+1:] yhw[hNs-1:] = fftbuffer[:hNs+1] fftbuffer = np.zeros(Ns) fftbuffer = np.real(ifft(Xr)) xrw[:hNs-1] = fftbuffer[hNs+1:] xrw[hNs-1:] = fftbuffer[:hNs+1] yh[ri:ri+Ns] += sw*yhw xr[ri:ri+Ns] += sw*xrw pin += H y = yh+xr return y, yh, xr
def hpsModel(x, fs, w, N, t, nH, minf0, maxf0, f0et, stocf):
hM1 = int(math.floor((w.size + 1) / 2))
hM2 = int(math.floor(w.size / 2))
Ns = 512
H = Ns // 4
hNs = Ns // 2
begin = max(hNs, hM1)
end = x.size - max(hNs, hM1)
fftbuffer = np.zeros(N)
yhw = np.zeros(Ns)
ystw = np.zeros(Ns)
yh = np.zeros(x.size)
yst = np.zeros(x.size)
w = w / sum(w)
sw = np.zeros(Ns)
ow = triang(2 * H)
sw[hNs - H:hNs + H] = ow
bh = blackmanharris(Ns)
bh = bh / sum(bh)
wr = bh
sw[hNs - H:hNs + H] = sw[hNs - H:hNs + H] / bh[hNs - H:hNs + H]
sws = H * hanning(Ns) / 2
hfreqp = []
f0t = 0
f0stable = 0
while begin < end:
# analyze
x1 = x[begin - hM1:begin + hM2]
mX, pX = DFT.dftAnal(x1, w, N)
ploc = U.peakDetection(mX, t)
iploc, ipmag, ipphase = U.peak_parabolicInterp(mX, pX, ploc)
ipfreq = fs * iploc / N
f0t = U.f0Twm(ipfreq, ipmag, f0et, minf0, maxf0, f0stable)
if ((f0stable==0)&(f0t>0)) \
or ((f0stable>0)&(np.abs(f0stable-f0t)<f0stable/5.0)):
f0stable = f0t
else:
f0stable = 0
hfreq, hmag, hphase = HM.harmonicDetection(ipfreq, ipmag, ipphase, f0t,
nH, hfreqp, fs)
hfreqp = hfreq
ri = begin - hNs - 1
xw2 = x[ri:ri + Ns] * wr
fftbuffer = np.zeros(Ns)
fftbuffer[:hNs] = xw2[hNs:]
fftbuffer[hNs:] = xw2[:hNs]
X2 = fft(fftbuffer)
# synth
Yh = U.genSinesSpectrum(hfreq, hmag, hphase, Ns, fs)
Xr = X2 - Yh
mXr = 20 * np.log10(abs(Xr[:hNs]))
mXrenv = resample(np.maximum(-200, mXr), mXr.size *
stocf) # decimate the spectrum to avoid -Inf
stocEnv = resample(mXrenv, hNs)
pYst = 2 * np.pi * np.random.rand(hNs)
Yst = np.zeros(Ns, dtype=complex)
Yst[:hNs] = 10**(stocEnv / 20) * np.exp(1j * pYst)
Yst[hNs + 1:] = 10**(stocEnv[:0:-1] / 20) * np.exp(-1j * pYst[:0:-1])
fftbuffer = np.zeros(Ns)
fftbuffer = np.real(ifft(Yh))
yhw[:hNs - 1] = fftbuffer[hNs + 1:]
yhw[hNs - 1:] = fftbuffer[:hNs + 1]
fftbuffer = np.zeros(Ns)
fftbuffer = np.real(ifft(Yst))
ystw[:hNs - 1] = fftbuffer[hNs + 1:]
ystw[hNs - 1:] = fftbuffer[:hNs + 1]
yh[ri:ri + Ns] += sw * yhw
yst[ri:ri + Ns] += sws * ystw
begin += H
y = yh + yst
return y, yh, yst