function ears = lyon2011_crosscouple(ears);
%LYON2011_CROSSCOUPLE adjust the intensity of the ear signals
% Usage: [CF, decim_naps, naps, BM, ohc, agc] = lyon2011(CF, input_waves, AGC_plot_fig_num, open_loop);
%
%
% Input parameters:
% detects : The CF struct holds the filterbank design and
% state; if you want to break the input up into
% segments, you need to use the updated CF
% to keep the state between segments.
% coeffs : 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. 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.
% state : Plot automatic gain control figure. Default is 0.
%
% Output parameters:
% state : The CF struct holds the filterbank design and
% state; if you want to break the input up into
% segments, you need to use the updated CF
% to keep the state between segments.
% update : decim_naps is like naps but time-decimated by
% the int CF.decimation.
%
%
% See also: lyon2011_agcstep lyon2011_carstep
% lyon2011_closeagcloop lyon2011_design
% lyon2011_ihcstep lyon2011_init
% lyon2011_spatialsmooth
% demo_lyon2011
%
% References:
% R. F. Lyon. Cascades of two-pole–two-zero asymmetric resonators are
% good models of peripheral auditory function. J. Acoust. Soc. Am.,
% 130(6), 2011.
%
%
% Url: http://amtoolbox.org/amt-1.4.0/doc/modelstages/lyon2011_crosscouple.php
% #Author: Amin Saremi (2016) adaptations for the AMT (based on <https://github.com/google/carfac>, Richard F. Lyon)
% #Author: Clara Hollomey (2021) adaptation for the AMT 1.0
% #License: gpl3
n_ears = length(ears);
if n_ears > 1
n_stages = ears(1).AGC_coeffs(1).n_AGC_stages;
% now cross-ear mix the stages that updated (leading stages at phase 0):
for stage = 1:n_stages
if ears(1).AGC_state(stage).decim_phase > 0
break % all recently updated stages are finished
else
mix_coeff = ears(1).AGC_coeffs(stage).AGC_mix_coeffs;
if mix_coeff > 0 % Typically stage 1 has 0 so no work on that one.
this_stage_sum = 0;
% sum up over the ears and get their mean:
for ear = 1:n_ears
stage_state = ears(ear).AGC_state(stage).AGC_memory;
this_stage_sum = this_stage_sum + stage_state;
end
this_stage_mean = this_stage_sum / n_ears;
% now move them all toward the mean:
for ear = 1:n_ears
stage_state = ears(ear).AGC_state(stage).AGC_memory;
ears(ear).AGC_state(stage).AGC_memory = ...
stage_state + mix_coeff * (this_stage_mean - stage_state);
end
end
end
end
end