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librosa.feature.chroma_cens

librosa.feature.chroma_cens(*, y=None, sr=22050, C=None, hop_length=512, fmin=None, tuning=None, n_chroma=12, n_octaves=7, bins_per_octave=36, cqt_mode='full', window=None, norm=2, win_len_smooth=41, smoothing_window='hann')

Computes the chroma variant “Chroma Energy Normalized” (CENS)

To compute CENS features, following steps are taken after obtaining chroma vectors using chroma_cqt: [1].

1. L-1 normalization of each chroma vector

2. Quantization of amplitude based on “log-like” amplitude thresholds

3. (optional) Smoothing with sliding window. Default window length = 41 frames

4. (not implemented) Downsampling

CENS features are robust to dynamics, timbre and articulation, thus these are commonly used in audio matching and retrieval applications.

[1]

Meinard Müller and Sebastian Ewert “Chroma Toolbox: MATLAB implementations for extracting variants of chroma-based audio features” In Proceedings of the International Conference on Music Information Retrieval (ISMIR), 2011.

Parameters:

ynp.ndarray [shape=(…, n,)]

audio time series. Multi-channel is supported.

srnumber > 0

sampling rate of y

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

a pre-computed constant-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

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

Column-wise normalization of the chromagram.

tuningfloat

Deviation (in fractions of a CQT bin) from A440 tuning

n_chromaint > 0

Number of chroma bins to produce

n_octavesint > 0

Number of octaves to analyze above fmin

windowNone or np.ndarray

Optional window parameter to filters.cq_to_chroma

bins_per_octaveint > 0

Number of bins per octave in the CQT.

Default: 36

cqt_mode[‘full’, ‘hybrid’]

Constant-Q transform mode

win_len_smoothint > 0 or None

Length of temporal smoothing window. None disables temporal smoothing. Default: 41

smoothing_windowstr, float or tuple

Type of window function for temporal smoothing. See librosa.filters.get_window for possible inputs. Default: ‘hann’

Returns:

censnp.ndarray [shape=(…, n_chroma, t)]

The output cens-chromagram

See also

chroma_cqt

Compute a chromagram from a constant-Q transform.

chroma_stft

Compute a chromagram from an STFT spectrogram or waveform.

librosa.filters.get_window

Compute a window function.

Examples

Compare standard cqt chroma to CENS.

>>> y, sr = librosa.load(librosa.ex('nutcracker'), duration=15)
>>> chroma_cens = librosa.feature.chroma_cens(y=y, sr=sr)
>>> chroma_cq = librosa.feature.chroma_cqt(y=y, sr=sr)

>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True)
>>> img = librosa.display.specshow(chroma_cq, y_axis='chroma', x_axis='time', ax=ax[0])
>>> ax[0].set(title='chroma_cq')
>>> ax[0].label_outer()
>>> librosa.display.specshow(chroma_cens, y_axis='chroma', x_axis='time', ax=ax[1])
>>> ax[1].set(title='chroma_cens')
>>> fig.colorbar(img, ax=ax)