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librosa.feature.chroma_vqt(*, y=None, sr=22050, V=None, hop_length=512, fmin=None, intervals, norm=inf, threshold=0.0, n_octaves=7, bins_per_octave=12, gamma=0)[source]

Variable-Q chromagram

This differs from CQT-based chroma by supporting non-equal temperament intervals.

Note: unlike CQT- and STFT-based chroma, VQT chroma does not aggregate energy from neighboring frequency bands. As a result, the number of chroma features produced is equal to the number of intervals used, or equivalently, the number of bins per octave in the underlying VQT representation.

ynp.ndarray [shape=(…, n,)]

audio time series. Multi-channel is supported.

srnumber > 0

sampling rate of y

Vnp.ndarray [shape=(…, d, t)] [Optional]

a pre-computed variable-Q spectrogram

hop_lengthint > 0

number of samples between successive chroma frames

fminfloat > 0

minimum frequency to analyze in the CQT. Default: C1 ~= 32.7 Hz

intervalsstr or array of floats in [1, 2)

Either a string specification for an interval set, e.g., ‘equal’, ‘pythagorean’, ‘ji3’, etc. or an array of intervals expressed as numbers between 1 and 2. .. see also:: librosa.interval_frequencies

normint > 0, +-np.inf, or None

Column-wise normalization of the chromagram.


Pre-normalization energy threshold. Values below the threshold are discarded, resulting in a sparse chromagram.

n_octavesint > 0

Number of octaves to analyze above fmin

bins_per_octaveint > 0, optional

Number of bins per octave in the VQT.

gammanumber > 0 [scalar]

Bandwidth offset for determining filter lengths. .. see also:: librosa.vqt

chromagramnp.ndarray [shape=(…, bins_per_octave, t)]

The output chromagram


Compare an equal-temperament CQT chromagram to a 5-limit just intonation chromagram. Both use 36 bins per octave.

>>> y, sr = librosa.load(librosa.ex('trumpet'))
>>> n_bins = 36
>>> chroma_cq = librosa.feature.chroma_cqt(y=y, sr=sr, n_chroma=n_bins)
>>> chroma_vq = librosa.feature.chroma_vqt(y=y, sr=sr,
...                                        intervals='ji5',
...                                        bins_per_octave=n_bins)
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(nrows=2, sharex=True)
>>> librosa.display.specshow(chroma_cq, y_axis='chroma', x_axis='time',
...                          ax=ax[0], bins_per_octave=n_bins)
>>> ax[0].set(ylabel='chroma_cqt')
>>> ax[0].label_outer()
>>> img = librosa.display.specshow(chroma_vq, y_axis='chroma_fjs', x_axis='time',
...                                ax=ax[1], bins_per_octave=n_bins,
...                                intervals='ji5')
>>> ax[1].set(ylabel='chroma_vqt')
>>> fig.colorbar(img, ax=ax)