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1 % Copyright 2012, Google, Inc.
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2 % Author Richard F. Lyon
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3 %
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4 % This Matlab file is part of an implementation of Lyon's cochlear model:
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5 % "Cascade of Asymmetric Resonators with Fast-Acting Compression"
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6 % to supplement Lyon's upcoming book "Human and Machine Hearing"
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7 %
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8 % Licensed under the Apache License, Version 2.0 (the "License");
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9 % you may not use this file except in compliance with the License.
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10 % You may obtain a copy of the License at
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11 %
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12 % http://www.apache.org/licenses/LICENSE-2.0
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13 %
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14 % Unless required by applicable law or agreed to in writing, software
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15 % distributed under the License is distributed on an "AS IS" BASIS,
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16 % WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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17 % See the License for the specific language governing permissions and
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18 % limitations under the License.
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19
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20 function [CF, decim_naps, naps] = CARFAC_Run ...
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21 (CF, input_waves, AGC_plot_fig_num)
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22 % function [CF, decim_naps, naps] = CARFAC_Run ...
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23 % (CF, input_waves, AGC_plot_fig_num)
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24 % This function runs the CARFAC; that is, filters a 1 or more channel
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25 % sound input to make one or more neural activity patterns (naps).
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26 %
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27 % The CF struct holds the filterbank design and state; if you want to
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28 % break the input up into segments, you need to use the updated CF
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29 % to keep the state between segments.
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30 %
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31 % input_waves is a column vector if there's just one audio channel;
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32 % more generally, it has a row per time sample, a column per audio channel.
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33 %
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34 % naps has a row per time sample, a column per filterbank channel, and
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35 % a layer per audio channel if more than 1.
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36 % decim_naps is like naps but time-decimated by the int CF.decimation.
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37 %
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38 % the input_waves are assumed to be sampled at the same rate as the
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39 % CARFAC is designed for; a resampling may be needed before calling this.
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40 %
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41 % The function works as an outer iteration on time, updating all the
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42 % filters and AGC states concurrently, so that the different channels can
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43 % interact easily. The inner loops are over filterbank channels, and
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44 % this level should be kept efficient.
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45
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46 [n_samp, n_ears] = size(input_waves);
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47 n_ch = CF.n_ch;
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48
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49 if nargin < 3
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50 AGC_plot_fig_num = 0;
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51 end
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52
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53 if n_ears ~= CF.n_ears
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54 error('bad number of input_waves channels passed to CARFAC_Run')
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55 end
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56
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57
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58 naps = zeros(n_samp, n_ch, n_ears);
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59
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60 seglen = 16;
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61 n_segs = ceil(n_samp / seglen);
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62
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63 if nargout > 1
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64 % make decimated detect output:
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65 decim_naps = zeros(n_segs, CF.n_ch, CF.n_ears);
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66 else
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67 decim_naps = [];
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68 end
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69
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70 if nargout > 2
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71 % make decimated detect output:
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72 naps = zeros(n_samp, CF.n_ch, CF.n_ears);
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73 else
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74 naps = [];
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75 end
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76
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77 for seg_num = 1:n_segs
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78 if seg_num == n_segs
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79 % The last segement may be short of seglen, but do it anyway:
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80 k_range = (seglen*(seg_num - 1) + 1):n_samp;
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81 else
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82 k_range = seglen*(seg_num - 1) + (1:seglen);
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83 end
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84 % Process a segment to get a slice of decim_naps, and plot AGC state:
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85 [seg_naps, CF] = CARFAC_Run_Segment(CF, input_waves(k_range, :));
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86
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87 if ~isempty(naps)
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88 for ear = 1:n_ears
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89 % Accumulate segment naps to make full naps
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90 naps(k_range, :, ear) = seg_naps(:, :, ear);
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91 end
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92 end
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93
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94 if ~isempty(decim_naps)
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95 for ear = 1:n_ears
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96 decim_naps(seg_num, :, ear) = CF.IHC_state(ear).ihc_accum / seglen;
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97 CF.IHC_state(ear).ihc_accum = zeros(n_ch,1);
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98 end
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99 end
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100
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101 if AGC_plot_fig_num
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102 figure(AGC_plot_fig_num); hold off; clf
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103 set(gca, 'Position', [.25, .25, .5, .5])
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104
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105 for ear = 1:n_ears
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106 plot(CF.AGC_state(ear).AGC_memory(:, 1), 'k-', 'LineWidth', 1)
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107 maxes(ear) = max(CF.AGC_state(ear).AGC_memory(:));
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108 hold on
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109 for stage = 1:3;
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110 plot(2^(stage-1) * (CF.AGC_state(ear).AGC_memory(:, stage) - ...
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111 2 * CF.AGC_state(ear).AGC_memory(:, stage+1)));
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112 end
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113 stage = 4;
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114 plot(2^(stage-1) * CF.AGC_state(ear).AGC_memory(:, stage));
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115 end
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116 axis([0, CF.n_ch+1, 0.0, max(maxes) + 0.01]);
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117 drawnow
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118 end
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119
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120 end
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121
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122
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123
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