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.

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::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