# config for high-resolution MFCC features, intended for neural network training
# Note: we keep all cepstra, so it has the same info as filterbank features,
# but MFCC is more easily compressible (because less correlated) which is why
# we prefer this method.
--use-energy=false   # use average of log energy, not energy.
--sample-frequency=16000
--num-mel-bins=40     # similar to Google's setup.
--num-ceps=40     # there is no dimensionality reduction.
--low-freq=20     # low cutoff frequency for mel bins... this is high-bandwidth data, so
                  # there might be some information at the low end.
--high-freq=-400 # high cutoff frequently, relative to Nyquist of 8000 (=7600)
