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<F>::ComputeFeatures()
Compute()
ExtractWindow() --> src/feat/Feature-window.cc
ProcessWindow()
Dither, remove_dc_offset, log_energy_pre_window, Preemphasize, windowcomputer_.Compute() --> src/feat/Feature-mfcc.cc
MfccComputer::Compute()
const MelBanks &mel_banks --> Mel-computations.cc
ComputerPowerSpectrum()
mel_banks.Compute()
mel_energies_.ApplyLog()
dct, cepstral_lifter