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librosa.feature.chroma_cqt

librosa.feature.chroma_cqt(y=None, sr=22050, C=None, hop_length=512, fmin=None, norm=inf, threshold=0.0, tuning=None, n_chroma=12, n_octaves=7, window=None, bins_per_octave=None, cqt_mode='full')

Constant-Q chromagram

Parameters

ynp.ndarray [shape=(n,)]

audio time series

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.

thresholdfloat

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

cqt_mode[‘full’, ‘hybrid’]

Constant-Q transform mode

Returns

chromagramnp.ndarray [shape=(n_chroma, t)]

The output chromagram

See also

librosa.util.normalize

librosa.core.cqt

librosa.core.hybrid_cqt

chroma_stft

Examples

Compare a long-window STFT chromagram to the CQT chromagram

>>> y, sr = librosa.load(librosa.util.example_audio_file(),
...                      offset=10, duration=15)
>>> chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr,
...                                           n_chroma=12, n_fft=4096)
>>> chroma_cq = librosa.feature.chroma_cqt(y=y, sr=sr)

>>> import matplotlib.pyplot as plt
>>> plt.figure()
>>> plt.subplot(2,1,1)
>>> librosa.display.specshow(chroma_stft, y_axis='chroma')
>>> plt.title('chroma_stft')
>>> plt.colorbar()
>>> plt.subplot(2,1,2)
>>> librosa.display.specshow(chroma_cq, y_axis='chroma', x_axis='time')
>>> plt.title('chroma_cqt')
>>> plt.colorbar()
>>> plt.tight_layout()
>>> plt.show()