view trunk/matlab/bmm/carfac/CARFAC_SAI.m @ 690:76f749d29b48

Fix memory leak in CARFAC.

Also get rid of most uses of auto, which tend to hurt readability
unless the type name is particularly long, especially when it masks
pointers.

% Copyright 2012 Google Inc. All Rights Reserved.
% 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 [sai_frame, sai_state, naps] = CARFAC_SAI(naps, k, sai_state, SAI_params)
% function sai = CARFAC_SAI(naps, k, sai_state, SAI_params)
%
% ...work in progress...
%
% Calculate the Stabilized Auditory Image from naps; 
% I think this is a binaural SAI by Steven Ness
%
% k seems to be a time index; it's an incremental update of the images...
% but this doesn't sound like a proper incremental approach...
%

[n_samp, n_ch, n_ears] = size(naps);

if nargin < 4
  SAI_params = struct( ...
    'frame_jump', 200, ...
    'sai_width', 500, ...
    'threshold_alpha', 0.99, ...
    'threshold_jump_factor', 1.2, ...
    'threshold_jump_offset', 0.1};
end

threshold_alpha = SAI_params.threshold_alpha;
threshold_jump = SAI_params.threshold_jump_factor;
threshold_offset = SAI_params.threshold_jump_offset;

sai2 = reshape(sai_state.sai, SAI_params.sai_width * n_ch, n_ears);
naps2 = reshape(naps, n_samp * n_ch, n_ears);

for ear = 1:n_ears
  data = naps(k, :, ear)';
  above_threshold = (sai_state(ear).lastdata > ...
    sai_state(ear).thresholds) & ...
    (sai_state(ear).lastdata > data);
  sai_state(ear).thresholds(above_threshold) = ...
    data(above_threshold) * threshold_jump + threshold_offset;
  sai_state(ear).thresholds(~above_threshold) = ...
    sai_state(ear).thresholds(~above_threshold) * threshold_alpha;
  sai_state(ear).lastdata = data;
  
  % Update SAI image with strobe data.
  otherear = 3 - ear;
  
  % Channels that are above the threhsold
  above_ch = find(above_threshold);
  
  % If we are above the threshold, set the trigger index and reset the
  % sai_index
  sai_state(ear).trigger_index(above_ch) = k;
  sai_state(ear).sai_index(above_ch) = 1;
  
  % Copy the right data from the nap to the sai
  chans = (1:n_ch)';
  fromindices = sai_state(ear).trigger_index() + (chans - 1) * n_samp;
  toindices = min((sai_state(ear).sai_index() + (chans - 1) * sai_params.sai_width), sai_params.sai_width * n_ch);
  sai2(toindices,ear) = naps2(fromindices, otherear);
  
  sai_state(ear).trigger_index(:) = sai_state(ear).trigger_index(:) + 1;
  sai_state(ear).sai_index(:) = sai_state(ear).sai_index(:) + 1;
end


sai_frame = reshape(sai2,sai_params.sai_width,n_ch,n_ears);
sai_state.sai = sai;  % probably this is not exactly what we want to store as state...