forked from https://github.com/jameslyons/python_speech_features

check the readme therein for the usages

It has been modified to produce the same results as with the compute-mfcc-feats and compute-fbank-feats (check their default parameters first) commands in Kaldi.

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The compute-mfcc-feats pipeline:

src/featbin/Compute-mfcc-feats.cc

Mfcc mfcc(mfcc_opts) --> src/feat/Feature-mfcc.h

struct MfccOptions

typedef OfflineFeatureTpl<MfccComputer> Mfcc --> src/feat/Feature-common.h

MfccComputer() --> src/feat/Feature-mfcc.cc

ComputeDctMatrix() --> src/matrix/Matrix-functions.cc

ComputeLifterCoeffs() --> src/feat/Mel-computations.cc

for each utterance: mfcc.ComputeFeatures()

src/feat/Feature-common-inl.h

   OfflineFeatureTpl<F>::ComputeFeatures()

Compute()

ExtractWindow() --> src/feat/Feature-window.cc

ProcessWindow()

Dither, remove_dc_offset, log_energy_pre_window, Preemphasize, window

           computer_.Compute() --> src/feat/Feature-mfcc.cc

MfccComputer::Compute()

                                        const MelBanks &mel_banks --> Mel-computations.cc

                                         srfft_                                                                                 ComputerPowerSpectrum()

mel_banks.Compute()

mel_energies.ApplyLog()

dct, cepstral_lifter