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# 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.
--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=7600 # high cutoff frequently, relative to Nyquist of 8000 (=7600)
--allow_downsample=true
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