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librosa.feature.melspectrogram

librosa.feature.melspectrogram(y=None, sr=22050, S=None, n_fft=2048, hop_length=512, win_length=None, window='hann', center=True, pad_mode='reflect', power=2.0, **kwargs)

Compute a mel-scaled spectrogram.

If a spectrogram input S is provided, then it is mapped directly onto the mel basis mel_f by mel_f.dot(S).

If a time-series input y, sr is provided, then its magnitude spectrogram S is first computed, and then mapped onto the mel scale by mel_f.dot(S**power). By default, power=2 operates on a power spectrum.

Parameters

ynp.ndarray [shape=(n,)] or None

audio time-series

srnumber > 0 [scalar]

sampling rate of y

Snp.ndarray [shape=(d, t)]

spectrogram

n_fftint > 0 [scalar]

length of the FFT window

hop_lengthint > 0 [scalar]

number of samples between successive frames. See librosa.core.stft

win_lengthint <= n_fft [scalar]

Each frame of audio is windowed by window(). The window will be of length win_length and then padded with zeros to match n_fft.

If unspecified, defaults to win_length = n_fft.

windowstring, tuple, number, function, or np.ndarray [shape=(n_fft,)]

• a window specification (string, tuple, or number); see scipy.signal.get_window

• a window function, such as scipy.signal.hanning

• a vector or array of length n_fft

centerboolean

• If True, the signal y is padded so that frame t is centered at y[t * hop_length].

• If False, then frame t begins at y[t * hop_length]

pad_modestring

If center=True, the padding mode to use at the edges of the signal. By default, STFT uses reflection padding.

powerfloat > 0 [scalar]

Exponent for the magnitude melspectrogram. e.g., 1 for energy, 2 for power, etc.

kwargsadditional keyword arguments

Mel filter bank parameters. See librosa.filters.mel for details.

Returns

Snp.ndarray [shape=(n_mels, t)]

Mel spectrogram

See also

librosa.filters.mel

Mel filter bank construction

librosa.core.stft

Short-time Fourier Transform

Examples

>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> librosa.feature.melspectrogram(y=y, sr=sr)
array([[  2.891e-07,   2.548e-03, ...,   8.116e-09,   5.633e-09],
       [  1.986e-07,   1.162e-02, ...,   9.332e-08,   6.716e-09],
       ...,
       [  3.668e-09,   2.029e-08, ...,   3.208e-09,   2.864e-09],
       [  2.561e-10,   2.096e-09, ...,   7.543e-10,   6.101e-10]])

Using a pre-computed power spectrogram would give the same result:

>>> D = np.abs(librosa.stft(y))**2
>>> S = librosa.feature.melspectrogram(S=D, sr=sr)

Display of mel-frequency spectrogram coefficients, with custom arguments for mel filterbank construction (default is fmax=sr/2):

>>> # Passing through arguments to the Mel filters
>>> S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128,
...                                     fmax=8000)

>>> import matplotlib.pyplot as plt
>>> plt.figure(figsize=(10, 4))
>>> S_dB = librosa.power_to_db(S, ref=np.max)
>>> librosa.display.specshow(S_dB, x_axis='time',
...                          y_axis='mel', sr=sr,
...                          fmax=8000)
>>> plt.colorbar(format='%+2.0f dB')
>>> plt.title('Mel-frequency spectrogram')
>>> plt.tight_layout()
>>> plt.show()