diff trunk/matlab/bmm/carfac/CARFAC_AGCStep.m @ 516:68c15d43fcc8

Added MATLAB code for Lyon's CAR-FAC filter cascade.

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/trunk/matlab/bmm/carfac/CARFAC_AGCStep.m	Wed Feb 15 21:26:40 2012 +0000
@@ -0,0 +1,82 @@
+% 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 state = CARFAC_AGCStep(AGC_coeffs, avg_detects, state)
+% function state = CARFAC_AGCStep(AGC_coeffs, avg_detects, state)
+%
+% one time step (at decimated low AGC rate) of the AGC state update
+
+n_AGC_stages = length(AGC_coeffs.AGC_epsilon);
+n_mics = length(state);
+n_ch = size(state(1).AGC_sum, 1);  % number of channels
+
+optimize_for_mono = n_mics == 1;  % mono optimization
+if ~optimize_for_mono
+  stage_sum = zeros(n_ch, 1);
+end
+
+for stage = 1:n_AGC_stages
+  if ~optimize_for_mono  % skip if mono
+    if stage > 1
+      prev_stage_mean = stage_sum / n_mics;
+    end
+    stage_sum(:) = 0;  % sum accumulating over mics at this stage
+  end
+  epsilon = AGC_coeffs.AGC_epsilon(stage);  % for this stage's LPF pole
+  polez1 = AGC_coeffs.AGC1_polez(stage);
+  polez2 = AGC_coeffs.AGC2_polez(stage);
+  for mic = 1:n_mics
+    if stage == 1
+      AGC_in = AGC_coeffs.detect_scale * avg_detects(:,mic);
+      AGC_in = max(0, AGC_in);  % don't let neg inputs in
+    else
+      % prev. stage mixed with prev_stage_sum
+      if optimize_for_mono
+        % Mono optimization ignores AGC_mix_coeff,
+        % assuming all(prev_stage_mean == AGC_memory(:, stage - 1));
+        % but we also don't even allocate or compute the sum or mean.
+        AGC_in = AGC_coeffs.AGC_stage_gain * ...
+          state(mic).AGC_memory(:, stage - 1);
+      else
+        AGC_in = AGC_coeffs.AGC_stage_gain * ...
+          (AGC_coeffs.AGC_mix_coeff * prev_stage_mean + ...
+            (1 - AGC_coeffs.AGC_mix_coeff) * ...
+              state(mic).AGC_memory(:, stage - 1));
+      end
+    end
+    AGC_stage = state(mic).AGC_memory(:, stage);
+    % first-order recursive smooting filter update:
+    AGC_stage = AGC_stage + epsilon * (AGC_in - AGC_stage);
+
+    % spatially spread it; using diffusion coeffs like in smooth1d
+    AGC_stage = SmoothDoubleExponential(AGC_stage, polez1, polez2);
+
+    state(mic).AGC_memory(:, stage) = AGC_stage;
+    if stage == 1
+      state(mic).sum_AGC = AGC_stage;
+    else
+      state(mic).sum_AGC = state(mic).sum_AGC + AGC_stage;
+    end
+    if ~optimize_for_mono
+      stage_sum = stage_sum + AGC_stage;
+    end
+  end
+end
+
+