def inver_cqt(cqt, dcs, nys, h_size):
CQIStand = standard.NSGIConstantQ(**kwargs)
recFrame = []
for j, i in enumerate(range(0,cqt.shape[0], h_size)):
cqt_frame = cqt[i:i+h_size]
# import pdb;pdb.set_trace()
inv_cqt_frame = CQIStand(cqt_frame.T, dcs[j], nys[j])
recFrame.append(inv_cqt_frame)
utils.progress(j, int(cqt.shape[0]/h_size), "Inverse Done")
frameSize = kwargs['inputSize']
y = recFrame[0]
invWindow = Windowing(type='triangular',normalized=False, zeroPhase=False)(standard.essentia.array(np.ones(frameSize)))
for i in range(1,len(recFrame)):
y = np.hstack([y,np.zeros(int(frameSize/2))])
y[-frameSize:] = y[-frameSize:] + recFrame[i]
utils.progress(i, len(recFrame), "Overlap Done")
y = y[int(frameSize/2):]
return y
def nsgicqgram(cq,
dc,
nf,
frameSize=8192,
transitionSize=1024,
minFrequency=65.41,
maxFrequency=6000,
binsPerOctave=48,
sampleRate=44100,
rasterize='full',
phaseMode='global',
gamma=0,
normalize='none',
window='hannnsgcq'):
"""Frame-wise invertible Constant-Q synthesis.
This code replicates the Sli-CQ algorithm from [1]. An inverse Tukey window
is used to resynthetise the original audio signal from the `nsgcqgram`
representation using the `NSGIConstantQ` algorithm.
References:
[1] Velasco, G. A., Holighaus, N., Dörfler, M., & Grill, T. (2011).
"Constructing an invertible constant-Q transform with non-stationary
Gabor frames". Proceedings of DAFX11, Paris, 93-99.
Args:
(list of 2D complex arrays): Time / frequency complex matrices representing the NSGCQ `constantq` coefficients for each `frameSize // 2` samples jump.
(list of complex vectors): Complex vectors representing the NSGCQ `constantqdc` coefficients for each `frameSize // 2` samples jump.
(list of complex vectors): Complex vectors representing the NSGCQ `constantqnf` coefficients for each `frameSize // 2` samples jump.
Returns:
audio (vector): The synthetized audio.
"""
hopSize = frameSize // 2
halfTransitionSize = transitionSize // 2
cqSize = cq[0].shape[1]
dcSize = len(dc[0])
nfSize = len(nf[0])
NSGICQS = es.NSGIConstantQ(inputSize=frameSize,
minFrequency=minFrequency,
maxFrequency=maxFrequency,
binsPerOctave=binsPerOctave,
sampleRate=sampleRate,
rasterize=rasterize,
phaseMode=phaseMode,
gamma=gamma,
normalize=normalize,
window=window,
minimumWindow=8)
# Tukey inverse window.
window = np.zeros(frameSize)
window[np.arange((hopSize + transitionSize) // 2,
(3 * hopSize - transitionSize) //
2)] = np.ones(hopSize - transitionSize)
window[np.hstack([
np.arange((hopSize - transitionSize) // 2,
(hopSize + transitionSize) // 2),
np.arange((3 * hopSize - transitionSize) // 2,
(3 * hopSize + transitionSize) // 2)
])] = __inverseTukeyWindow__(
np.arange(-transitionSize, transitionSize) / transitionSize)
# Undo the frame centering.
cqShiftEven = np.hstack(
[np.arange(3 * cqSize // 4, cqSize),
np.arange(0, 3 * cqSize // 4)])
dcShiftEven = np.hstack(
[np.arange(3 * dcSize // 4, dcSize),
np.arange(0, 3 * dcSize // 4)])
nfShiftEven = np.hstack(
[np.arange(3 * nfSize // 4, nfSize),
np.arange(0, 3 * nfSize // 4)])
cqShiftOdd = np.hstack(
[np.arange(cqSize // 4, cqSize),
np.arange(0, cqSize // 4)])
dcShiftOdd = np.hstack(
[np.arange(dcSize // 4, dcSize),
np.arange(0, dcSize // 4)])
nfShiftOdd = np.hstack(
[np.arange(nfSize // 4, nfSize),
np.arange(0, nfSize // 4)])
cqShift = [
cq[i][:, cqShiftEven] if i % 2 else cq[i][:, cqShiftOdd]
for i in range(len(cq))
]
dcShift = [
dc[i][dcShiftEven] if i % 2 else dc[i][dcShiftOdd]
for i in range(len(dc))
]
nfShift = [
nf[i][nfShiftEven] if i % 2 else nf[i][nfShiftOdd]
for i in range(len(nf))
]
# Loop to store the audio frames.
frames = [
NSGICQS(cqShift[i], dcShift[i], nfShift[i]) for i in range(len(cq))
]
# Overlap-add.
audio = np.zeros((len(frames) + 1) * hopSize)
for kk in range(len(frames)):
audio[np.arange(int(np.ceil((kk - .5) * hopSize)),
int(np.ceil((kk + 1.5) * hopSize)))] += np.roll(
frames[kk], int(np.floor(
((-1)**kk) * hopSize / 2))) * window
return audio[hopSize:]