Mercurial > hg > aimc
view trunk/matlab/bmm/carfac/CARFAC_Run_Segment.m @ 704:e9855b95cd04
Small cleanup of eigen usage in SAI implementation.
author | ronw@google.com |
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date | Tue, 16 Jul 2013 19:56:11 +0000 |
parents | 3e2e0ab4f708 |
children |
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% Copyright 2012, Google, Inc. % 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 [naps, CF, BM, seg_ohc, seg_agc] = CARFAC_Run_Segment(... CF, input_waves, open_loop) % function [naps, CF, BM, seg_ohc, seg_agc] = CARFAC_Run_Segment(... % CF, input_waves, open_loop) % % This function runs the CARFAC; that is, filters a 1 or more channel % sound input segment to make one or more neural activity patterns (naps); % it can be called multiple times for successive segments of any length, % as long as the returned CF with modified state is passed back in each % time. % % input_waves is a column vector if there's just one audio channel; % more generally, it has a row per time sample, a column per audio channel. % % naps has a row per time sample, a column per filterbank channel, and % a layer per audio channel if more than 1. % BM is basilar membrane motion (filter outputs before detection). % % the input_waves are assumed to be sampled at the same rate as the % CARFAC is designed for; a resampling may be needed before calling this. % % The function works as an outer iteration on time, updating all the % filters and AGC states concurrently, so that the different channels can % interact easily. The inner loops are over filterbank channels, and % this level should be kept efficient. % % seg_ohc seg_agc are optional extra outputs useful for seeing what the % ohc nonlinearity and agc are doing; both in terms of extra damping. if nargin < 3 open_loop = 0; end if nargout > 2 do_BM = 1; else do_BM = 0; end [n_samp, n_ears] = size(input_waves); if n_ears ~= CF.n_ears error('bad number of input_waves channels passed to CARFAC_Run') end n_ch = CF.n_ch; naps = zeros(n_samp, n_ch, n_ears); % allocate space for result if do_BM BM = zeros(n_samp, n_ch, n_ears); seg_ohc = zeros(n_samp, n_ch, n_ears); seg_agc = zeros(n_samp, n_ch, n_ears); end detects = zeros(n_ch, n_ears); for k = 1:n_samp % at each time step, possibly handle multiple channels for ear = 1:n_ears % This would be cleaner if we could just get and use a reference to % CF.ears(ear), but Matlab doesn't work that way... [car_out, CF.ears(ear).CAR_state] = CARFAC_CAR_Step( ... input_waves(k, ear), CF.ears(ear).CAR_coeffs, CF.ears(ear).CAR_state); % update IHC state & output on every time step, too [ihc_out, CF.ears(ear).IHC_state] = CARFAC_IHC_Step( ... car_out, CF.ears(ear).IHC_coeffs, CF.ears(ear).IHC_state); % run the AGC update step, decimating internally, [CF.ears(ear).AGC_state, updated] = CARFAC_AGC_Step( ... ihc_out, CF.ears(ear).AGC_coeffs, CF.ears(ear).AGC_state); % save some output data: naps(k, :, ear) = ihc_out; % output to neural activity pattern if do_BM BM(k, :, ear) = car_out; state = CF.ears(ear).CAR_state; seg_ohc(k, :, ear) = state.zA_memory; seg_agc(k, :, ear) = state.zB_memory;; end end % connect the feedback from AGC_state to CAR_state when it updates; % all ears together here due to mixing across them: if updated if n_ears > 1 % do multi-aural cross-coupling: CF.ears = CARFAC_Cross_Couple(CF.ears); end if ~open_loop CF = CARFAC_Close_AGC_Loop(CF); end end end