icqt

diffsptk.ICQT

alias of InverseConstantQTransform

class diffsptk.InverseConstantQTransform(frame_period: int, sample_rate: int, *, f_min: float = 32.7, n_bin: float = 84, n_bin_per_octave: int = 12, tuning: float = 0, filter_scale: float = 1, norm: float = 1, sparsity: float = 0.01, window: str = 'hann', scale: bool = True, res_type: str | None = 'kaiser_best', **kwargs)

Perform inverse constant-Q transform based on the librosa implementation.

Parameters:

frame_periodint >= 1

The frame period in samples, \(P\).

sample_rateint >= 1

The sample rate in Hz.

f_minfloat > 0

The minimum center frequency in Hz.

n_binint >= 1

The number of CQ-bins, \(K\).

n_bin_per_octaveint >= 1

The number of bins per octave, \(B\).

tuningfloat

The tuning offset in fractions of a bin.

filter_scalefloat > 0

The filter scale factor.

normfloat

The type of norm used in the basis function normalization.

sparsityfloat in [0, 1)

The sparsification factor.

windowstr

The window function for the basis.

scalebool

If True, scale the CQT response by the length of the filter.

res_type[‘kaiser_best’, ‘kaiser_fast’] or None

The resampling type.

**kwargsadditional keyword arguments

See torchaudio.transforms.Resample.

forward(c: Tensor, out_length: int | None = None) → Tensor

Compute inverse constant-Q transform.

Parameters:

cTensor [shape=(…, T/P, K)]

The CQT complex input.

out_lengthint or None

The length of the output waveform.

Returns:

outTensor [shape=(…, T)]

The reconstructed waveform.

Examples

>>> x = diffsptk.sin(99)
>>> cqt = diffsptk.CQT(100, 8000, n_bin=4)
>>> icqt = diffsptk.ICQT(100, 8000, n_bin=4)
>>> y = icqt(cqt(x), out_length=x.size(0))
>>> y.shape
torch.Size([100])

See also

cqt istft