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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='reflect', res_type=None, 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.

ynp.ndarray [shape=(n,)]

audio time series

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.

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.


Padding mode for centered frame analysis.

See also: librosa.stft and numpy.pad.


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.

CQTnp.ndarray [shape=(n_bins, t), dtype=np.float]

Constant-Q energy for each frequency at each time.


If hop_length is not an integer multiple of 2**(n_bins / bins_per_octave)

Or if y is too short to support the frequency range of the CQT.

See also



This function caches at level 20.