Mercurial > hg > aimc
view trunk/matlab/bmm/carfac/CARFAC_Run_Segment.m @ 534:95a11cca4619
Add CARFAC_Design_Doc.txt, CARFAC_Run_Segment.m, and some renames; rename various variables to be more parallel; clean up init code and such.
author | dicklyon@google.com |
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date | Fri, 16 Mar 2012 04:19:24 +0000 |
parents | |
children | 2964a3b4a00a |
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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] = CARFAC_Run_Segment(CF, input_waves) % function [naps, CF, decim_naps] = CARFAC_Run_Segment(CF, input_waves) % % 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. % decim_naps is like naps but time-decimated by the int CF.decimation. % % 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. % % See other functions for designing and characterizing the CARFAC: % CF = CARFAC_Design(fs, CF_CAR_params, CF_AGC_params, n_ears) % transfns = CARFAC_Transfer_Functions(CF, to_chans, from_chans) [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 detects = zeros(n_ch, n_ears); for k = 1:n_samp % at each time step, possibly handle multiple channels for ear = 1:n_ears [car_out, CF.CAR_state(ear)] = CARFAC_CAR_Step( ... input_waves(k, ear), CF.CAR_coeffs, CF.CAR_state(ear)); % update IHC state & output on every time step, too [ihc_out, CF.IHC_state(ear)] = CARFAC_IHC_Step( ... car_out, CF.IHC_coeffs, CF.IHC_state(ear)); detects(:, ear) = ihc_out; % for input to AGC, and out to SAI naps(k, :, ear) = ihc_out; % output to neural activity pattern end % run the AGC update step, taking input from IHC_state, % decimating internally, all ears at once due to mixing across them: [CF.AGC_state, updated] = CARFAC_AGC_Step( ... CF.AGC_coeffs, detects, CF.AGC_state); % connect the feedback from AGC_state to CAR_state when it updates if updated CF = CARFAC_Close_AGC_Loop(CF); end end