clc;clear;train_feature0=[];  %A类特征train_feature1=[];  %B类特征%%%%%相关参数选取%%%%%N=128;ine = 1*10^(-3);  %%%%%%%%%A类训练数据root0=‘D:\model_train\train_orign‘;   list0=dir(root0);list0(1:2)=[];N0=length(list0);for i=1:N0   file=[root0,‘\‘,list0(i).name];   x=readwav(file);       [temp]=mfcc(x);     train_feature0=[train_feature0;temp];       disp(list0(i).name);end%%%%    [vector0,cent0]=VQ(train_feature0,N,ine);VQ聚类A类%%%%% for i=1:N% train_feature=[train_feature;vector0(i).data(1:XQ,:)];% end% label0=ones(N*XQ,1);%文件标记 1%%%%%%%%B类训练数据root1=‘D:\model_train\train_tamper‘;list1=dir(root1);list1(1:2)=[];N1=length(list1);for j=1:N1   file=[root1,‘\‘,list1(j).name];   x=wavread(file);       [temp]=mfcc(x);   train_feature1=[train_feature1;temp];   disp(list1(j).name);end%%%%%%%%%%%VQ聚类B类 [vector1,cent1]=VQ(train_feature1,N,ine);

clc;clear;%---------用SVM测试三种特征的准确率，单独检测，2012-3-19-------addpath(‘svm-mat-2.89-3‘); % add path with libsvm routinesdisplay(datestr(now));%------------------------装载训练数据----------------------------------------load(‘train_feature.mat‘);  %语音特征训练数据load(‘train_label.mat‘);%对应标签load(‘test_feature.mat‘);  %用于归一化%-------------直接指定参数-----------G = 1/24; C = 1e8; %lecmd=sprintf(‘-t 2 -g %.4e -c %.4e‘,G,C);    [train_scale,test_scale] = scaleForSVM_corrected(train_feature,test_feature,-1,1);% scale the training set and testing sets to [0,1]model1=svmtrain(train_label,train_scale,cmd);% train the svm classifier  训练 按帧save model1.mat model1;save test_scale.mat test_scale;clear;load(‘model1.mat‘); load(‘test_scale.mat‘); %------------------------装载测试数据----------------------------------------load(‘test_label.mat‘);%对应标签load(‘test_file_frames.mat‘); %帧级别load(‘test_label_frame.mat‘);%--------------------------------------------------------------------------[predict_label,predict_accuracy_rate]=svmpredict(test_label_frame,test_scale,model1);% classify the testing set  N=length(test_file_frames);temp1=0;temp2=0;for i=1:N    temp2=temp2+test_file_frames(i);    temp1=temp2-test_file_frames(i)+1;  %  zonghe=score(temp1:temp2);    same=ismember(predict_label(temp1:temp2),test_label_frame(temp1:temp2));     if  sum(same)/test_file_frames(i)>0.5            predict_result(i)=test_label(i);        else          predict_result(i)=(~test_label(i));        endendpredict_result=predict_result‘;index=(predict_result==test_label);num_correct=sum(index);accuracy=num_correct/N;fprintf(‘A类和B类综合 accuracy = %0.2f  (%s%s%s)%s \n‘,accuracy,num2str(num_correct),‘/‘,num2str(N),‘(classification)‘);display(datestr(now));%%%%%%%%%%%%%%%%%%%A类准确率index=(predict_result(1:200)==test_label(1:200));num_z=sum(index);accuracy=num_z/(N/2);fprintf(‘A类 accuracy = %0.2f  (%s%s%s)%s \n‘,accuracy,num2str(num_z),‘/‘,num2str(N/2),‘(classification)‘);%%%%%%%%%%%%%%%%%%%B类准确率index=(predict_result(201:400)==test_label(201:400));num_f=sum(index);accuracy=num_f/(N/2);fprintf(‘B类 accuracy = %0.2f  (%s%s%s)%s \n‘,accuracy,num2str(num_f),‘/‘,num2str(N/2),‘(classification)‘);