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librosa.feature.inverse.mfcc_to_mel¶
- librosa.feature.inverse.mfcc_to_mel(mfcc, n_mels=128, dct_type=2, norm='ortho', ref=1.0, lifter=0)[source]¶
Invert Mel-frequency cepstral coefficients to approximate a Mel power spectrogram.
This inversion proceeds in two steps:
The inverse DCT is applied to the MFCCs
core.db_to_power is applied to map the dB-scaled result to a power spectrogram
- 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
- Returns
- Mnp.ndarray [shape=(n_mels, n)]
An approximate Mel power spectrum recovered from mfcc
- Warns
- UserWarning
due to critical values in lifter array that invokes underflow.
See also
mfcc
melspectrogram
scipy.fftpack.dct