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librosa.feature.inverse.mfcc_to_audio¶
- librosa.feature.inverse.mfcc_to_audio(mfcc, n_mels=128, dct_type=2, norm='ortho', ref=1.0, lifter=0, **kwargs)[source]¶
Convert Mel-frequency cepstral coefficients to a time-domain audio signal
This function is primarily a convenience wrapper for the following steps:
Convert mfcc to Mel power spectrum (
mfcc_to_mel
)Convert Mel power spectrum to time-domain audio (
mel_to_audio
)
- Parameters
- mfccnp.ndarray [shape=(n_mfcc, n)]
The Mel-frequency cepstral coefficients
- n_melsint > 0
The number of Mel frequencies
- dct_type{1, 2, 3}
Discrete cosine transform (DCT) type By default, DCT type-2 is used.
- normNone or ‘ortho’
If dct_type is 2 or 3, setting norm=’ortho’ uses an orthonormal DCT basis.
Normalization is not supported for dct_type=1.
- refnumber or callable
Reference power for (inverse) decibel calculation
- lifternumber >= 0
If lifter>0, apply inverse liftering (inverse cepstral filtering): M[n, :] <- M[n, :] / (1 + sin(pi * (n + 1) / lifter)) * lifter / 2
- kwargsadditional keyword arguments
Parameters to pass through to
mel_to_audio
- Returns
- ynp.ndarray [shape=(n)]
A time-domain signal reconstructed from mfcc
See also
mfcc_to_mel
mel_to_audio
feature.mfcc
core.griffinlim
scipy.fftpack.dct