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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='fft', 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.

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

Constant-Q representation as produced by cqt

hop_lengthint > 0 [scalar]

number of samples between successive frames

fminfloat > 0 [scalar]

Minimum frequency. Defaults to C1 ~= 32.70 Hz

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.


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.


Resampling mode. By default, this uses 'fft' mode for high-quality reconstruction, but this may be slow depending on your signal duration. 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.

ynp.ndarray, [shape=(n_samples), dtype=np.float]

Audio time-series reconstructed from the CQT representation.


This function caches at level 40.


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)