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librosa.hybrid_cqt

librosa.hybrid_cqt(y, *, sr=22050, hop_length=512, fmin=None, n_bins=84, bins_per_octave=12, tuning=0.0, filter_scale=1, norm=1, sparsity=0.01, window='hann', scale=True, pad_mode='constant', res_type='soxr_hq', dtype=None)[source]

Compute the hybrid constant-Q transform of an audio signal.

Here, the hybrid CQT uses the pseudo CQT for higher frequencies where the hop_length is longer than half the filter length and the full CQT for lower frequencies.

Parameters:

ynp.ndarray [shape=(…, n)]

audio time series. Multi-channel is supported.

srnumber > 0 [scalar]

sampling rate of y

hop_lengthint > 0 [scalar]

number of samples between successive CQT columns.

fminfloat > 0 [scalar]

Minimum frequency. Defaults to C1 ~= 32.70 Hz

n_binsint > 0 [scalar]

Number of frequency bins, starting at fmin

bins_per_octaveint > 0 [scalar]

Number of bins per octave

tuningNone or float

Tuning offset in fractions of a bin.

If None, tuning will be automatically estimated from the signal.

The minimum frequency of the resulting CQT will be modified to fmin * 2**(tuning / bins_per_octave).

filter_scalefloat > 0

Filter filter_scale factor. Larger values use longer windows.

norm{inf, -inf, 0, float > 0}

Type of norm to use for basis function normalization. See librosa.util.normalize.

sparsityfloat in [0, 1)

Sparsify the CQT basis by discarding up to sparsity fraction of the energy in each basis.

Set sparsity=0 to disable sparsification.

windowstr, tuple, number, or function

Window specification for the basis filters. See filters.get_window for details.

scalebool

If True, scale the CQT response by square-root the length of each channel’s filter. This is analogous to norm='ortho' in FFT.

If False, do not scale the CQT. This is analogous to norm=None in FFT.

pad_modestring

Padding mode for centered frame analysis.

See also: librosa.stft and numpy.pad.

res_typestring

Resampling mode. See librosa.cqt for details.

dtypenp.dtype, optional

The complex dtype to use for computing the CQT. By default, this is inferred to match the precision of the input signal.

Returns:

CQTnp.ndarray [shape=(…, n_bins, t), dtype=np.float]: Constant-Q energy for each frequency at each time.