librosa.feature.mfcc

librosa.feature.mfcc(y=None, sr=22050, S=None, n_mfcc=20, **kwargs)[source]

Mel-frequency cepstral coefficients

Parameters:

y : np.ndarray [shape=(n,)] or None

audio time series

sr : number > 0 [scalar]

sampling rate of y

S : np.ndarray [shape=(d, t)] or None

log-power Mel spectrogram

n_mfcc: int > 0 [scalar]

number of MFCCs to return

kwargs : additional keyword arguments

Arguments to melspectrogram, if operating on time series input

Returns:

M : np.ndarray [shape=(n_mfcc, t)]

MFCC sequence

See also

melspectrogram

Examples

Generate mfccs from a time series

>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> librosa.feature.mfcc(y=y, sr=sr)
array([[ -5.229e+02,  -4.944e+02, ...,  -5.229e+02,  -5.229e+02],
       [  7.105e-15,   3.787e+01, ...,  -7.105e-15,  -7.105e-15],
       ...,
       [  1.066e-14,  -7.500e+00, ...,   1.421e-14,   1.421e-14],
       [  3.109e-14,  -5.058e+00, ...,   2.931e-14,   2.931e-14]])

Use a pre-computed log-power Mel spectrogram

>>> S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128,
...                                    fmax=8000)
>>> librosa.feature.mfcc(S=librosa.power_to_db(S))
array([[ -5.207e+02,  -4.898e+02, ...,  -5.207e+02,  -5.207e+02],
       [ -2.576e-14,   4.054e+01, ...,  -3.997e-14,  -3.997e-14],
       ...,
       [  7.105e-15,  -3.534e+00, ...,   0.000e+00,   0.000e+00],
       [  3.020e-14,  -2.613e+00, ...,   3.553e-14,   3.553e-14]])

Get more components

>>> mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40)

Visualize the MFCC series

>>> import matplotlib.pyplot as plt
>>> plt.figure(figsize=(10, 4))
>>> librosa.display.specshow(mfccs, x_axis='time')
>>> plt.colorbar()
>>> plt.title('MFCC')
>>> plt.tight_layout()

(Source code)

../_images/librosa-feature-mfcc-1.png