acousticfeatures.m

传感器网络探测声信号通过特征提取进行目标设别分类

clear all;
%-----------------------------------------------------------------------------------------------
%Feature Extaction specific
j=1;
%fileName = textread('e:\data\BAE\filelist\ml_bae_filelist1.txt','%s','delimiter','\n','whitespace','');
%source = 'bae';	
for k=3:3,
   switch k
   case 1,
      fileName = textread('d:\acousticq1.txt','%s','delimiter','\n','whitespace','');
		source = 'q1';
		type = char(['v'*ones(1,152) 'n'*ones(1,127)]);		% 'v' (vehicle) or 'n' (noise)
   case 2,
      fileName = textread('d:\acousticq2.txt','%s','delimiter','\n','whitespace','');
		source = 'q2';
		type = char(['v'*ones(1,137) 'n'*ones(1,116)]);		% 'v' (vehicle) or 'n' (noise)
   case 3,
      fileName = textread('d:\acousticq3.txt','%s','delimiter','\n','whitespace','');
		source = 'q3';
		type = char(['v'*ones(1,131) 'n'*ones(1,224)]);		% 'v' (vehicle) or 'n' (noise)
	end
Datadir = 'd:\';

Fs = 4960;
%Fs = 1024;

nclasses=3;
%-----------------------------------------------------------------------------------------------

FRmat = [];
Frmatb = [];
Frmatc = [];
Fmat = [];
Fmatb = [];
Fmatc = [];
fileCount = length(fileName);
fprintf('\nNo of Files are %d\n',i, fileCount);
for i = 1:fileCount
   % extract all features for this run
   clear sdata;
   filen = fileName{i};
   load([Datadir fileName{i}]);	% sdata 
   [trash,filen] = strtok(filen,'\');
   [trash,filen] = strtok(filen,'\');
   [trash,filen] = strtok(filen,'\');
   [trash,filen] = strtok(filen,'\');
 	[filen,trash] = strtok(filen,'\');
   eval(['sdata = ' filen '; clear ' filen ';']);
   fprintf('\nfile no:(%d) %s of type [%s]\n',i, fileName{i},type(i));

   [F,FR] =  afm_mlpatternGen(sdata,Fs,type(i));
   
   FR = randomize(FR);
   F = randomize(F);

	FRmat = [FRmat; FR];
%   Frmatb = [Frmatb; mean(FR,1) j];
%   Frmatc = [Frmatc; median(FR,1) j];
%	Fmat = [Fmat; F j*ones(size(F,1),1)];
%   Fmatb = [Fmatb; mean(F,1) j];
%   Fmatc = [Fmatc; median(F,1) j];

	j=j+1;
end


 
 FRmat = randomize(FRmat);
% Frmatb = randomize(Frmatb);
% Frmatc = randomize(Frmatc);
% Fmat = randomize(Fmat);
% Fmatb = randomize(Fmatb);
% Fmatc = randomize(Fmatc);

% eval(['save ' Datadir 'Feature_2a_' source ' Frmatb -ascii']);  % save partitioned patterns based on runs
% eval(['save ' Datadir 'Feature_2b_' source ' Frmatc -ascii']);  % save partitioned patterns based on runs
 eval(['save ' Datadir 'Feature_1_' source ' FRmat -ascii']);  % save partitioned patterns based on runs
% eval(['save ' Datadir 'Feature_7a_' source ' Fmatb']);  % save partitioned patterns based on runs
% eval(['save ' Datadir 'Feature_7b_' source ' Fmatc']);  % save partitioned patterns based on runs
% eval(['save ' Datadir 'Feature_6_' source ' Fmat']);  % save partitioned patterns based on runs
 
 end