dicklyon@615: % Copyright 2013, Google, Inc.
dicklyon@615: % Author: Richard F. Lyon
dicklyon@615: %
dicklyon@615: % This Matlab file is part of an implementation of Lyon's cochlear model:
dicklyon@615: % "Cascade of Asymmetric Resonators with Fast-Acting Compression"
dicklyon@615: % to supplement Lyon's upcoming book "Human and Machine Hearing"
dicklyon@615: %
dicklyon@615: % Licensed under the Apache License, Version 2.0 (the "License");
dicklyon@615: % you may not use this file except in compliance with the License.
dicklyon@615: % You may obtain a copy of the License at
dicklyon@615: %
dicklyon@615: %     http://www.apache.org/licenses/LICENSE-2.0
dicklyon@615: %
dicklyon@615: % Unless required by applicable law or agreed to in writing, software
dicklyon@615: % distributed under the License is distributed on an "AS IS" BASIS,
dicklyon@615: % WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
dicklyon@615: % See the License for the specific language governing permissions and
dicklyon@615: % limitations under the License.
dicklyon@615: 
dicklyon@615: function layer_array = SAI_UpdateBuffers(layer_array, seg_naps, seg_num)
dicklyon@615: % function layer_array = SAI_UpdateBuffers(layer_array, seg_naps, seg_num)
dicklyon@615: %
dicklyon@615: % Input/Output: layer_array contains all the coefficients and state for
dicklyon@615: % the layer of different time scales of SAI;
dicklyon@615: % we might want to separate these as in CARFAC.
dicklyon@615: %
dicklyon@615: % seg_naps is a new segmeent of NAP from the CAR-FAC to shift into the
dicklyon@615: % first layer.  Each subsequent layer gets input off the input end of the
dicklyon@615: % previous layer, with smoothing and decimation.
dicklyon@615: %
dicklyon@615: % The segment index seg_num is used to control sub-sampled updates of
dicklyon@615: % the larger-scale layers.
dicklyon@615: 
dicklyon@615: n_layers = length(layer_array);
dicklyon@615: [seg_len, n_nap_ch] = size(seg_naps);
dicklyon@615: 
dicklyon@615: % Array of what to shift in to first or next layer.
dicklyon@615: new_chunk = seg_naps;
dicklyon@615: 
dicklyon@619: gain = 1.05;  % gain from layer to layer; could be layer dependent.
dicklyon@615: 
dicklyon@615: %% 
dicklyon@615: % Decimate using a 2-3-4-filter and partial differencing emphasize onsets:
dicklyon@615: kernel = filter([1 1]/2, 1, filter([1 1 1]/3, 1, [1 1 1 1 0 0 0 0]/4));
dicklyon@665: % kernel = kernel + 2*diff([0, kernel]);
dicklyon@615: % figure(1)
dicklyon@615: % plot(kernel)
dicklyon@615: 
dicklyon@615: %% 
dicklyon@615: for layer = 1:n_layers
dicklyon@615:   [n_lags, n_ch] = size(layer_array(layer).nap_buffer);
dicklyon@615:   if (n_nap_ch ~= n_ch)
dicklyon@615:     error('Wrong number of channels in nap_buffer.');
dicklyon@615:   end
dicklyon@615:   
dicklyon@615:   interval = layer_array(layer).update_interval;
dicklyon@615:   if (0 == mod(seg_num, interval))
dicklyon@615:     % Account for 2X decimation and infrequent updates; find number of time
dicklyon@615:     % points to shift in.  Tolerate slip of a fraction of a sample.
dicklyon@615:     n_shift = seg_len * interval / (2.0^(layer - 1));
dicklyon@615:     if layer > 1
dicklyon@615:       % Add the leftover fraction before floor.
dicklyon@615:       n_shift = n_shift + layer_array(layer).nap_fraction;
dicklyon@615:       layer_array(layer).nap_fraction = n_shift - floor(n_shift);
dicklyon@615:       n_shift = floor(n_shift);
dicklyon@615:       % Grab new stuff from new end (big time indices) of previous layer.
dicklyon@615:       % Take twice as many times as we need, + 5, for decimation, and do
dicklyon@665:       % smoothing to get new points.
dicklyon@615:       new_chunk = ...
dicklyon@615:         layer_array(layer - 1).nap_buffer((end - 2*n_shift - 4):end, :);
dicklyon@615:       new_chunk = filter(kernel, 1, new_chunk);
dicklyon@665:       % new_chunk = gain * new_chunk(7:2:end, :);
dicklyon@665:       % try a little extra smoothing:
dicklyon@665:       new_chunk = gain * (new_chunk(7:2:end, :) + new_chunk(6:2:(end-1), :))/2;
dicklyon@615:       
dicklyon@615:     end
dicklyon@615:     % Put new stuff in at latest time indices.
dicklyon@615:     layer_array(layer).nap_buffer = ...
dicklyon@615:       [layer_array(layer).nap_buffer((1 + n_shift):end, :); ...
dicklyon@615:       new_chunk];  % this should fit just right if we have n_shift new times.
dicklyon@615:   end
dicklyon@615: end
dicklyon@615: 
dicklyon@615: return
dicklyon@615: