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librosa.feature.inverse.mel_to_audio

librosa.feature.inverse.mel_to_audio(M, *, sr=22050, n_fft=2048, hop_length=None, win_length=None, window='hann', center=True, pad_mode='constant', power=2.0, n_iter=32, length=None, dtype=<class 'numpy.float32'>, **kwargs)[source]

Invert a mel power spectrogram to audio using Griffin-Lim.

This is primarily a convenience wrapper for:

>>> S = librosa.feature.inverse.mel_to_stft(M)
>>> y = librosa.griffinlim(S)

Parameters:

Mnp.ndarray [shape=(…, n_mels, n), non-negative]: The spectrogram as produced by feature.melspectrogram
srnumber > 0 [scalar]: sampling rate of the underlying signal
n_fftint > 0 [scalar]: number of FFT components in the resulting STFT
hop_lengthNone or int > 0: The hop length of the STFT. If not provided, it will default to n_fft // 4
win_lengthNone or int > 0: The window length of the STFT. By default, it will equal n_fft
windowstring, tuple, number, function, or np.ndarray [shape=(n_fft,)]: A window specification as supported by stft or istft
centerboolean: If True, the STFT is assumed to use centered frames. If False, the STFT is assumed to use left-aligned frames.
pad_modestring: If center=True, the padding mode to use at the edges of the signal. By default, STFT uses zero padding.
powerfloat > 0 [scalar]: Exponent for the magnitude melspectrogram
n_iterint > 0: The number of iterations for Griffin-Lim
lengthNone or int > 0: If provided, the output y is zero-padded or clipped to exactly length samples.
dtypenp.dtype: Real numeric type for the time-domain signal. Default is 32-bit float.
**kwargsadditional keyword arguments for Mel filter bank parameters
n_melsint > 0 [scalar]: number of Mel bands to generate
fminfloat >= 0 [scalar]: lowest frequency (in Hz)
fmaxfloat >= 0 [scalar]: highest frequency (in Hz). If None, use fmax = sr / 2.0
htkbool [scalar]: use HTK formula instead of Slaney
norm{None, ‘slaney’, or number} [scalar]: If ‘slaney’, divide the triangular mel weights by the width of the mel band (area normalization). If numeric, use librosa.util.normalize to normalize each filter by to unit l_p norm. See librosa.util.normalize for a full description of supported norm values (including +-np.inf). Otherwise, leave all the triangles aiming for a peak value of 1.0

Returns:

ynp.ndarray [shape(…, n,)]: time-domain signal reconstructed from M