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function code = train(traindir, n) % Speaker Recognition: Training Stage % % Input: % traindir : string name of directory contains all train sound files % n : number of train files

function test(testdir, n, code) % Speaker Recognition: Testing Stage % % Input: % testdir : string name of directory contains all test sound files % n : number of test files in testd

/********************************************************* // 模糊C均值聚类算法 // Coded by zhchshen // Date: 2008-08-19 23:42 // zhchshen@yahoo.com.cn // Bezdek J C. Pattern Recognition with Fuzzy

% Pattern Recognition Tools % Version 4.0.14 04-Mar-2005 % %Datasets and Mappings (just most important routines) %--------------------- %dataset Define and retrieve dataset from datamatrix and lab

% Pattern Recognition Tools % Version URV 24-Mar-2004 % % This is prelimanary, many support routines in ./private ./@datasets % and ./@mappings are not mentioned. % %Datasets and Mappings (just most i

% Pattern Recognition Tools % Version URV 24-Mar-2004 % % This is prelimanary, many support routines in ./private ./@datasets % and ./@mappings are not mentioned. % %Datasets and Mappings (just most i

% Pattern Recognition Tools % Version 4.0.14 04-Mar-2005 % %Datasets and Mappings (just most important routines) %--------------------- %dataset Define and retrieve dataset from datamatrix and lab

% generate some simple pattern recognition example data % Copyright 1999 by Todd K. Moon nclass = 5; ndata = 300; dim = 2; %$$$ means = []; %$$$ R = []; %$$$ C = []; %$$$ for n=1:nclass

% Test the neural network on a pattern recognition problem % % Copyright 1999 by Todd K. Moon % Make some training data d = []; x = []; xl = 0:.1:1; for i=1:100 x1 = rand * 1.4 - .2; x2