Caution

You're reading an old version of this documentation. If you want up-to-date information, please have a look at 0.10.2.

librosa.icqt

librosa.icqt(C, *, sr=22050, hop_length=512, fmin=None, bins_per_octave=12, tuning=0.0, filter_scale=1, norm=1, sparsity=0.01, window='hann', scale=True, length=None, res_type='soxr_hq', dtype=None)[source]

Compute the inverse constant-Q transform.

Given a constant-Q transform representation C of an audio signal y, this function produces an approximation y_hat.

Parameters:

Cnp.ndarray, [shape=(…, n_bins, n_frames)]

Constant-Q representation as produced by cqt

srnumber > 0 [scalar]

sampling rate of the signal

hop_lengthint > 0 [scalar]

number of samples between successive frames

fminfloat > 0 [scalar]

Minimum frequency. Defaults to C1 ~= 32.70 Hz

bins_per_octaveint > 0 [scalar]

Number of bins per octave

tuningfloat [scalar]

Tuning offset in fractions of a bin.

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

filter_scalefloat > 0 [scalar]

Filter scale factor. Small values (<1) use shorter windows for improved time resolution.

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.

lengthint > 0, optional

If provided, the output y is zero-padded or clipped to exactly length samples.

res_typestring

Resampling mode. See librosa.resample for supported modes.

dtypenumeric type

Real numeric type for y. Default is inferred to match the numerical precision of the input CQT.

Returns:

ynp.ndarray, [shape=(…, n_samples), dtype=np.float]: Audio time-series reconstructed from the CQT representation.

See also

cqt
librosa.resample

Notes

This function caches at level 40.

Examples

Using default parameters

>>> y, sr = librosa.load(librosa.ex('trumpet'))
>>> C = librosa.cqt(y=y, sr=sr)
>>> y_hat = librosa.icqt(C=C, sr=sr)

Or with a different hop length and frequency resolution:

>>> hop_length = 256
>>> bins_per_octave = 12 * 3
>>> C = librosa.cqt(y=y, sr=sr, hop_length=256, n_bins=7*bins_per_octave,
...                 bins_per_octave=bins_per_octave)
>>> y_hat = librosa.icqt(C=C, sr=sr, hop_length=hop_length,
...                 bins_per_octave=bins_per_octave)