## This config is given by conf/make_pitch_online.sh to the program compute-and-process-kaldi-pitch-feats,
  ## and is copied by steps/online/nnet2/prepare_online_decoding.sh and similar scripts, to be given
  ## to programs like online2-wav-nnet2-latgen-faster.
  ## The program compute-and-process-kaldi-pitch-feats will use it to compute pitch features that
  ## are the same as that those which will generated in online decoding; this enables us to train
  ## in a way that's compatible with online decoding.
  ## 
  
  ## most of these options relate to the post-processing rather than the pitch
  ## extraction itself.
  --add-raw-log-pitch=true   ## this is intended for input to neural nets, so our
                             ## approach is "throw everything in and see what
                             ## sticks".
  --normalization-left-context=75
  --normalization-right-context=50 # We're removing some of the right-context
                                   # for the normalization.   Would normally be 75.
                                   #
                                   # Note: our changes to the (left,right) context
                                   # from the defaults of (75,75) to (75,50) will
                                   # almost certainly worsen results, but will
                                   # reduce latency.
  --frames-per-chunk=10    ## relates to offline simulation of online decoding; 1
                           ## would be equivalent to getting in samples one by
                           ## one.
  --simulate-first-pass-online=true  ## this make the online-pitch-extraction code
                                     ## output the 'first-pass' features, which
                                     ## are less accurate than the final ones, and
                                     ## which are the only features the neural-net
                                     ## decoding would ever see (since we can't
                                     ## afford to do lattice rescoring in the
                                     ## neural-net code