cqt#

diffsptk.CQT#: alias of ConstantQTransform

class diffsptk.ConstantQTransform(frame_period, sample_rate, *, f_min=32.7, n_bin=84, n_bin_per_octave=12, tuning=0, filter_scale=1, norm=1, sparsity=0.01, window='hann', scale=True, **kwargs)[source]#

Perform constant-Q transform based on the librosa implementation.

Parameters:

frame_peirodint >= 1: Frame period in samples, \(P\).
sample_rateint >= 1: Sample rate in Hz.
f_minfloat > 0: Minimum center frequency in Hz.
n_binint >= 1: Number of CQ-bins, \(K\).
n_bin_per_octaveint >= 1: number of bins per octave, \(B\).
tuningfloat: Tuning offset in fractions of a bin.
filter_scalefloat > 0: Filter scale factor.
normfloat: Type of norm used in basis function normalization.
sparsityfloat in [0, 1): Sparsification factor.
windowstr: Window function for the basis.
scalebool: If True, scale the CQT responce by the length of filter.
**kwargsadditional keyword arguments: See torchaudio.transforms.Resample.

forward(x)[source]#

Compute constant-Q transform.

Parameters:

xTensor [shape=(…, T)]: Waveform.

Returns:

outTensor [shape=(…, T/P, K)]: CQT complex output.

Examples

>>> x = diffsptk.sin(99)
>>> cqt = diffsptk.CQT(100, 8000, n_bin=4)
>>> c = cqt(x).abs()
>>> c
tensor([[1.1259, 1.2069, 1.3008, 1.3885]])