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librosa.feature.inverse.mel_to_stft

librosa.feature.inverse.mel_to_stft(M, sr=22050, n_fft=2048, power=2.0, **kwargs)

Approximate STFT magnitude from a Mel power spectrogram.

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

powerfloat > 0 [scalar]

Exponent for the magnitude melspectrogram

kwargsadditional keyword arguments

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

Returns

Snp.ndarray [shape=(n_fft, t), non-negative]

An approximate linear magnitude spectrogram

See also

feature.melspectrogram

core.stft

filters.mel

util.nnls

Examples

>>> y, sr = librosa.load(librosa.util.example_audio_file(), duration=5, offset=10)
>>> S = np.abs(librosa.stft(y))
>>> mel_spec = librosa.feature.melspectrogram(S=S, sr=sr)
>>> S_inv = librosa.feature.inverse.mel_to_stft(mel_spec, sr=sr)

Compare the results visually

>>> import matplotlib.pyplot as plt
>>> plt.figure()
>>> plt.subplot(2,1,1)
>>> librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max, top_db=None),
...                          y_axis='log', x_axis='time')
>>> plt.colorbar()
>>> plt.title('Original STFT')
>>> plt.subplot(2,1,2)
>>> librosa.display.specshow(librosa.amplitude_to_db(np.abs(S_inv - S),
...                                                  ref=S.max(), top_db=None),
...                          vmax=0, y_axis='log', x_axis='time', cmap='magma')
>>> plt.title('Residual error (dB)')
>>> plt.colorbar()
>>> plt.tight_layout()
>>> plt.show()