% Y = SoundClassificationModelDS(dir, nS, nB [, OPTIONAL ARGS])
%                                                                    
% Train multiple SoundModelDS using using Baum-Welch re-estimation.
%
%Inputs:
%
% DIR - root directory for training data (contains sub-directories of individual class data)
% nS - number of initial model states for each HMM [10]
% nB  - number of basis functions to extract [10]
%
% The following variables are optional, and are specified using
% 'parameter' value pairs on the command line.
%
%    'hopSize'          'PT10N1000F'
%    'loEdge'            62.5,      
%    'hiEdge'            16000,     
%    'octaveResolution'  '1/8'
%    'outputFile'         'SoundClassificationModel'
%    'sequenceHopSize'      '',
%    'sequenceFrameLength'  ''
%
% Outputs:
%
% Y - cell array of SoundModelDS structures, one per sound class
%
% Copyright (C) 1999-2002 Michael A. Casey, All Rights Reserved.


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