tom@516: % Copyright 2012, Google, Inc.
tom@516: % Author: Richard F. Lyon
tom@516: %
tom@516: % This Matlab file is part of an implementation of Lyon's cochlear model:
tom@516: % "Cascade of Asymmetric Resonators with Fast-Acting Compression"
tom@516: % to supplement Lyon's upcoming book "Human and Machine Hearing"
tom@516: %
tom@516: % Licensed under the Apache License, Version 2.0 (the "License");
tom@516: % you may not use this file except in compliance with the License.
tom@516: % You may obtain a copy of the License at
tom@516: %
tom@516: %     http://www.apache.org/licenses/LICENSE-2.0
tom@516: %
tom@516: % Unless required by applicable law or agreed to in writing, software
tom@516: % distributed under the License is distributed on an "AS IS" BASIS,
tom@516: % WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
tom@516: % See the License for the specific language governing permissions and
tom@516: % limitations under the License.
tom@516: 
tom@516: function [CF, sai] = CARFAC_SAI(CF, k, n_mics, naps, sai)
tom@516: % function sai = CARFAC_SAI(CF_struct, n_mics, naps, sai)
tom@516: %
tom@516: % Calculate the Stabilized Auditory Image from naps
tom@516: %
tom@516: 
tom@516:   threshold_alpha = CF.sai_params.threshold_alpha;
tom@516:   threshold_jump = CF.sai_params.threshold_jump_factor;
tom@516:   threshold_offset = CF.sai_params.threshold_jump_offset;
tom@516: 
tom@516:   sai2 = reshape(sai,CF.sai_params.sai_width * CF.n_ch,n_mics);
tom@516:   naps2 = reshape(naps,CF.n_samp * CF.n_ch,n_mics);
tom@516: 
tom@516:   for mic = 1:n_mics
tom@516:     data = naps(k, :, mic)';
tom@516:     above_threshold = (CF.sai_state(mic).lastdata > ...
tom@516:                        CF.sai_state(mic).thresholds) & ...
tom@516:                       (CF.sai_state(mic).lastdata > data);
tom@516:     CF.sai_state(mic).thresholds(above_threshold) = ...
tom@516:         data(above_threshold) * threshold_jump + threshold_offset;
tom@516:     CF.sai_state(mic).thresholds(~above_threshold) = ...
tom@516:         CF.sai_state(mic).thresholds(~above_threshold) * threshold_alpha;
tom@516:     CF.sai_state(mic).lastdata = data;
tom@516: 
tom@516:     % Update SAI image with strobe data.
tom@516:     othermic = 3 - mic;
tom@516: 
tom@516:     % Channels that are above the threhsold
tom@516:     above_ch = find(above_threshold);
tom@516: 
tom@516:     % If we are above the threshold, set the trigger index and reset the
tom@516:     % sai_index
tom@516:     CF.sai_state(mic).trigger_index(above_ch) = k;
tom@516:     CF.sai_state(mic).sai_index(above_ch) = 1;
tom@516: 
tom@516:     % Copy the right data from the nap to the sai
tom@516:     chans = (1:CF.n_ch)';
tom@516:     fromindices = CF.sai_state(mic).trigger_index() + (chans - 1) * CF.n_samp;
tom@516:     toindices = min((CF.sai_state(mic).sai_index() + (chans - 1) * ...
tom@516:                      CF.sai_params.sai_width), ...
tom@516:                      CF.sai_params.sai_width * CF.n_ch);
tom@516:     sai2(toindices,mic) = naps2(fromindices,othermic);
tom@516: 
tom@516:     CF.sai_state(mic).trigger_index(:) = CF.sai_state(mic).trigger_index(:) + 1;
tom@516:     CF.sai_state(mic).sai_index(:) = CF.sai_state(mic).sai_index(:) + 1;
tom@516: 
tom@516:   end
tom@516: 
tom@516:   sai = reshape(sai2,CF.sai_params.sai_width,CF.n_ch,n_mics);
tom@516:   naps = reshape(naps2,CF.n_samp, CF.n_ch,n_mics);
tom@516: