tom@455: % Copyright 2012, Google, Inc.
tom@455: % Author: Richard F. Lyon
tom@455: %
tom@455: % This Matlab file is part of an implementation of Lyon's cochlear model:
tom@455: % "Cascade of Asymmetric Resonators with Fast-Acting Compression"
tom@455: % to supplement Lyon's upcoming book "Human and Machine Hearing"
tom@455: %
tom@455: % Licensed under the Apache License, Version 2.0 (the "License");
tom@455: % you may not use this file except in compliance with the License.
tom@455: % You may obtain a copy of the License at
tom@455: %
tom@455: %     http://www.apache.org/licenses/LICENSE-2.0
tom@455: %
tom@455: % Unless required by applicable law or agreed to in writing, software
tom@455: % distributed under the License is distributed on an "AS IS" BASIS,
tom@455: % WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
tom@455: % See the License for the specific language governing permissions and
tom@455: % limitations under the License.
tom@455: 
dicklyon@456: function [sai_frame, sai_state, naps] = CARFAC_SAI(naps, k, sai_state, SAI_params)
dicklyon@456: % function sai = CARFAC_SAI(naps, k, sai_state, SAI_params)
tom@455: %
dicklyon@474: % ...work in progress...
dicklyon@474: %
dicklyon@456: % Calculate the Stabilized Auditory Image from naps; 
dicklyon@456: % I think this is a binaural SAI by Steven Ness
dicklyon@456: %
dicklyon@456: % k seems to be a time index; it's an incremental update of the images...
dicklyon@456: % but this doesn't sound like a proper incremental approach...
tom@455: %
tom@455: 
dicklyon@474: [n_samp, n_ch, n_ears] = size(naps);
tom@455: 
dicklyon@456: if nargin < 4
dicklyon@456:   SAI_params = struct( ...
dicklyon@456:     'frame_jump', 200, ...
dicklyon@456:     'sai_width', 500, ...
dicklyon@456:     'threshold_alpha', 0.99, ...
dicklyon@456:     'threshold_jump_factor', 1.2, ...
dicklyon@456:     'threshold_jump_offset', 0.1};
dicklyon@456: end
tom@455: 
dicklyon@456: threshold_alpha = SAI_params.threshold_alpha;
dicklyon@456: threshold_jump = SAI_params.threshold_jump_factor;
dicklyon@456: threshold_offset = SAI_params.threshold_jump_offset;
tom@455: 
dicklyon@474: sai2 = reshape(sai_state.sai, SAI_params.sai_width * n_ch, n_ears);
dicklyon@474: naps2 = reshape(naps, n_samp * n_ch, n_ears);
tom@455: 
dicklyon@474: for ear = 1:n_ears
dicklyon@474:   data = naps(k, :, ear)';
dicklyon@474:   above_threshold = (sai_state(ear).lastdata > ...
dicklyon@474:     sai_state(ear).thresholds) & ...
dicklyon@474:     (sai_state(ear).lastdata > data);
dicklyon@474:   sai_state(ear).thresholds(above_threshold) = ...
dicklyon@456:     data(above_threshold) * threshold_jump + threshold_offset;
dicklyon@474:   sai_state(ear).thresholds(~above_threshold) = ...
dicklyon@474:     sai_state(ear).thresholds(~above_threshold) * threshold_alpha;
dicklyon@474:   sai_state(ear).lastdata = data;
dicklyon@456:   
dicklyon@456:   % Update SAI image with strobe data.
dicklyon@474:   otherear = 3 - ear;
dicklyon@456:   
dicklyon@456:   % Channels that are above the threhsold
dicklyon@456:   above_ch = find(above_threshold);
dicklyon@456:   
dicklyon@456:   % If we are above the threshold, set the trigger index and reset the
dicklyon@456:   % sai_index
dicklyon@474:   sai_state(ear).trigger_index(above_ch) = k;
dicklyon@474:   sai_state(ear).sai_index(above_ch) = 1;
dicklyon@456:   
dicklyon@456:   % Copy the right data from the nap to the sai
dicklyon@456:   chans = (1:n_ch)';
dicklyon@474:   fromindices = sai_state(ear).trigger_index() + (chans - 1) * n_samp;
dicklyon@474:   toindices = min((sai_state(ear).sai_index() + (chans - 1) * sai_params.sai_width), sai_params.sai_width * n_ch);
dicklyon@474:   sai2(toindices,ear) = naps2(fromindices, otherear);
dicklyon@456:   
dicklyon@474:   sai_state(ear).trigger_index(:) = sai_state(ear).trigger_index(:) + 1;
dicklyon@474:   sai_state(ear).sai_index(:) = sai_state(ear).sai_index(:) + 1;
dicklyon@456: end
tom@455: 
tom@455: 
dicklyon@474: sai_frame = reshape(sai2,sai_params.sai_width,n_ch,n_ears);
dicklyon@456: sai_state.sai = sai;  % probably this is not exactly what we want to store as state...
tom@455: 
tom@455: