Updates to cepstral features (MFCC and GFCC)

30 Dec 2016

Working towards the next Essentia release, we have updated our cepstral features. The updates include:

• Support for extracting MFCCs ‘the htk way’ (python example).

• In literature there are two common MFCCs ‘standards’ differing in some parameters and the mel-scale computation itself: the Slaney way (Auditory toolbox) and the htk way (chapter 5.4 from htk book).

• See a python notebook for a comparison with mfcc extracted with librosa and with htk.

• Support for inverting the computed MFCCs back to spectral (mel) domain (python example).

• The first MFCC coefficients are standard for describing singing voice timbre. The MFCC feature vector however does not represent the singing voice well visually. Instead, it is a common practice to invert the first 12-15 MFCC coefficients back to mel-bands domain for visualization. We have ported invmelfcc.m as explained here.

• Support for cent scale.

You can start using these features before the official release by building Essentia from the master branch.

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