name: extract_feature
synopsis: extract_feature [options] [arguments]

description:  
this is the acoustic front-end for the ISIP prototype system.
users can control various parameters from the command line.

arguments: 
    -delta                : generate first derivative coefficients (off)
    -acc                  : generate second derivative
                            coefficients                           (off) 
    -cms                  : use cepstral mean subtraction          (off)
    -energy               : include the log average energy         (off) 
    -zero_mean            : zero-mean the source to debias         (off) 
    -energy_norm          : normalize the energy                   (off)
    -mfcc N               : number of mel-spaced cepstral
                            coefficients                           (1)
    -delta_win N          : the length of the regression window
                            used to calculate the derivatives      (2) 
    -lifter_coeff N       : number of liftering coefficients       (22) 
    -window_dur N         : duration of the analysis window        (20 msecs) 
    -frame_dur N          : duration of the analysis frame         (10 msecs) 
    -num_fbanks N         : number of filterbanks to apply         (24)
    -pre_emph_coeff F     : the pre-emphasis coefficient to use
                            for a filter of the form 1 - F*Z^-1    (1.0)
    -sf SF                : the signal sample frequency            (8000.0)
    -window_type name     : hamming, rectangle, hanning            (hamming) 
    -output_mode mode     : ascii, binary                          (ascii) 
    -swap_bytes           : byte swap the input data               (off)
    -input input.list     : list of input (raw audio) files
    -output output.list   : list of output (feature) files 
    -vtln                 : perform vocal tract length normalization
    -vtln_factor F        : bilinear transform warping factor      (1.0)
    -bilinear             : use the bilinear transform VTLN
    -c0                   : output c0 to the feature vector
    -help                 : display this message
Example:

extract_feature -delta -acc -cms -zero_mean -energy -energy_norm -mfcc 12 -delta_win 2 -lifter_coeff 22 -window_dur 25 -frame_dur 10 -num_fbanks 24 -window_type hamming -sf 8000.0 -pre_emph_coeff  0.97 -output_mode binary -input raw.list -output mfcc.list 

This would generate a 39 dimensional feature vector, with 12 mfcc's, 1 energy 
and their delta and acceleration coefficients using cepstral mean 
subtraction and energy normalization.

Note that extract_feature can currently only accept 16-bit linear headerless
raw binary files as input.

man page: http://www.isip.msstate.edu/projects/speech/education/toolkits/asr/front_end/extract_feature/extract_feature.html
