view matlab/bmm/carfac/SmoothDoubleExponential.m @ 593:40934f897a56

Fixed certain minor documentation bugs.
Added the CAR::designFilters and CAR::stageG methods. These methods design the CAR.coeff coefficients. They have been compared to be the same as the matlab coefficients.
An Ear is now contructed with a specific FS or, it uses the default.
Added the PsychoAcoustics class to do ERB and Hz conversions.
Added the EarTest.C main which allows the construction of an Ear class for testing.

% Copyright 2012 Google Inc. All Rights Reserved.
% 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 signal_vecs = SmoothDoubleExponential(signal_vecs, ...
  polez1, polez2, fast_matlab_way)
% function signal_vecs = SmoothDoubleExponential(signal_vecs, ...
%   polez1, polez2, fast_matlab_way)
%
% Smooth the input column vectors in signal_vecs using forward
% and backwards one-pole smoothing filters, backwards first, with
% approximately reflecting edge conditions.
%
% It will be done with Matlab's filter function if "fast_matlab_way"
% is nonzero or defaulted; use 0 to test the algorithm for how to do it
% in sequential c code.

if nargin < 4
  fast_matlab_way = 1;
  % can also use the slow way with explicit loop like we'll do in C++
end

if fast_matlab_way
  [junk, Z_state] = filter(1-polez1, [1, -polez1], ...
    signal_vecs((end-10):end, :));  % initialize state from 10 points
  [signal_vecs(end:-1:1), Z_state] = filter(1-polez2, [1, -polez2], ...
    signal_vecs(end:-1:1), Z_state*polez2/polez1);
  signal_vecs = filter(1-polez1, [1, -polez1], signal_vecs, ...
    Z_state*polez1/polez2);
else
  npts = size(signal_vecs, 1);
  state = zeros(size(signal_vecs, 2));
  for index = npts-10:npts
    input = signal_vecs(index, :);
    state = state + (1 - polez1) * (input - state);
  end
  % smooth backward with polez2, starting with state from above:
  for index = npts:-1:1
    input = signal_vecs(index, :);
    state = state + (1 - polez2) * (input - state);
    signal_vecs(index, :) = state;
  end
  % smooth forward with polez1, starting with state from above:
  for index = 1:npts
    input = signal_vecs(index, :);
    state = state + (1 - polez1) * (input - state);
    signal_vecs(index, :) = state;
  end
end