annotate trunk/matlab/bmm/carfac/CARFAC_Run_Segment.m @ 548:ff12d0432d9c

Implemented all but g0 initialisation in CAR class

% Copyright 2012, Google, Inc.
% Author Richard F. Lyon
%
% This Matlab file is part of an implementation of Lyon's cochlear model:
% "Cascade of Asymmetric Resonators with Fast-Acting Compression"
% to supplement Lyon's upcoming book "Human and Machine Hearing"
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
%     http://www.apache.org/licenses/LICENSE-2.0
%
% Unless required by applicable law or agreed to in writing, software
% distributed under the License is distributed on an "AS IS" BASIS,
% WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
% See the License for the specific language governing permissions and
% limitations under the License.

function [naps, CF, BM] = CARFAC_Run_Segment(CF, input_waves, open_loop)
% function [naps, CF, BM] = CARFAC_Run_Segment(CF, input_waves, open_loop)
% 
% This function runs the CARFAC; that is, filters a 1 or more channel
% sound input segment to make one or more neural activity patterns (naps);
% it can be called multiple times for successive segments of any length,
% as long as the returned CF with modified state is passed back in each
% time.
%
% input_waves is a column vector if there's just one audio channel;
% more generally, it has a row per time sample, a column per audio channel.
%
% naps has a row per time sample, a column per filterbank channel, and
% a layer per audio channel if more than 1.
% BM is basilar membrane motion (filter outputs before detection).
%
% the input_waves are assumed to be sampled at the same rate as the
% CARFAC is designed for; a resampling may be needed before calling this.
%
% The function works as an outer iteration on time, updating all the
% filters and AGC states concurrently, so that the different channels can
% interact easily.  The inner loops are over filterbank channels, and
% this level should be kept efficient.
%
% See other functions for designing and characterizing the CARFAC:
% CF = CARFAC_Design(fs, CF_CAR_params, CF_AGC_params, n_ears)
% transfns = CARFAC_Transfer_Functions(CF, to_chans, from_chans)

if nargin < 3
  open_loop = 0;
end

if nargout > 2
  do_BM = 1;
else
  do_BM = 0;
end

[n_samp, n_ears] = size(input_waves);

if n_ears ~= CF.n_ears
  error('bad number of input_waves channels passed to CARFAC_Run')
end

n_ch = CF.n_ch;
naps = zeros(n_samp, n_ch, n_ears);  % allocate space for result
if do_BM
  BM = zeros(n_samp, n_ch, n_ears);
end

detects = zeros(n_ch, n_ears);
for k = 1:n_samp
  % at each time step, possibly handle multiple channels
  for ear = 1:n_ears
    [car_out, CF.CAR_state(ear)] = CARFAC_CAR_Step( ...
      input_waves(k, ear), CF.CAR_coeffs, CF.CAR_state(ear));
    
    % update IHC state & output on every time step, too
    [ihc_out, CF.IHC_state(ear)] = CARFAC_IHC_Step( ...
      car_out, CF.IHC_coeffs, CF.IHC_state(ear));
    
    detects(:, ear) = ihc_out;  % for input to AGC, and out to SAI
    naps(k, :, ear) = ihc_out;  % output to neural activity pattern  
    if do_BM
      BM(k, :, ear) = car_out;
    end
  end
  % run the AGC update step, taking input from IHC_state, 
  % decimating internally, all ears at once due to mixing across them:
  [CF.AGC_state, updated] = CARFAC_AGC_Step( ...
    CF.AGC_coeffs, detects, CF.AGC_state);
  
  % connect the feedback from AGC_state to CAR_state when it updates
  if updated & ~open_loop
    CF = CARFAC_Close_AGC_Loop(CF);
  end
end