语音信号处理实验报告

语音信号处理实验 班级： 学号： 姓名：实验一 基于MATLAB的语音信号时域特征分析（2学时）1） 短时能量 （1）加矩形窗 a=wavread(mike.wav); a=a(:,1);subplot(6,1,1),plot(a);N=32;for i=2:6h=linspace(1,1,2.(i-2)*N);%形成一个矩形窗，长度为2.(i-2)*N En=conv(h,a.*a);% 求短时能量函数En subplot(6,1,i),plot(En);if(i=2) ,legend(N=32);elseif(i=3), legend(N=64);elseif(i=4) ,legend(N=128);elseif(i=5) ,legend(N=256);elseif(i=6) ,legend(N=512);endend（2）加汉明窗 a=wavread(mike.wav);a=a(:,1);subplot(6,1,1),plot(a);N=32;for i=2:6h=hanning(2.(i-2)*N);%形成一个汉明窗，长度为2.(i-2)*N En=conv(h,a.*a);% 求短时能量函数En subplot(6,1,i),plot(En);if(i=2), legend(N=32);elseif(i=3), legend(N=64);elseif(i=4) ,legend(N=128);elseif(i=5) ,legend(N=256);elseif(i=6) ,legend(N=512);endend2） 短时平均过零率a=wavread(mike.wav);a=a(:,1);n=length(a);N=320;subplot(3,1,1),plot(a);h=linspace(1,1,N);En=conv(h,a.*a); %求卷积得其短时能量函数Ensubplot(3,1,2),plot(En); for i=1:n-1 if a(i)=0 b(i)= 1; else b(i) = -1; end if a(i+1)=0 b(i+1)=1; else b(i+1)= -1; end w(i)=abs(b(i+1)-b(i); %求出每相邻两点符号的差值的绝对值 end k=1; j=0;while (k+N-1)n Zm(k)=0; for i=0:N-1; Zm(k)=Zm(k)+w(k+i); end j=j+1; k=k+N/2; %每次移动半个窗 end for w=1:j Q(w)=Zm(160*(w-1)+1)/(2*N); %短时平均过零率 end subplot(3,1,3),plot(Q),grid;3） 自相关函数 N=240y=wavread(mike.wav);y=y(:,1);x=y(13271:13510);x=x.*rectwin(240);R=zeros(1,240);for k=1:240for n=1:240-kR(k)=R(k)+x(n)*x(n+k);endend j=1:240;plot(j,R);grid;实验二 基于MATLAB分析语音信号频域特征1）短时谱 cleara=wavread(mike.wav);a=a(:,1);subplot(2,1,1),plot(a);title(original signal);gridN=256;h=hamming(N);for m=1:N b(m)=a(m)*h(m)end y=20*log(abs(fft(b)subplot(2,1,2)plot(y);title(短时谱);grid2）语谱图 x,fs,nbits=wavread(mike.wav) x=x(:,1);specgram(x,512,fs,100); xlabel(时间(s);ylabel(频率(Hz);title(语谱图);3）倒谱和复倒谱（1）加矩形窗时的倒谱和复倒谱cleara=wavread(mike.wav,4000,4350);a=a(:,1);N=300;h=linspace(1,1,N);for m=1:Nb(m)=a(m)*h(m);end c=cceps(b);c=fftshift(c);d=rceps(b);d=fftshift(d); subplot(2,1,1)plot(d);title(加矩形窗时的倒谱)subplot(2,1,2)plot(c);title(加矩形窗时的复倒谱)（2）加汉明窗时的倒谱和复倒谱cleara=wavread(mike.wav,4000,4350);a=a(;,1);N=300;h=hamming(N);for m=1:Nb(m)=a(m)*h(m);end c=cceps(b);c=fftshift(c);d=rceps(b);d=fftshift(d); subplot(2,1,1)plot(d);title(加汉明窗时的倒谱)subplot(2,1,2)plot(c);title(加汉明窗时的复倒谱)实验三 基于MATLAB的LPC分析MusicSource = wavread(mike.wav);MusicSource=MusicSource(:,1);Music_source = MusicSource;N = 256; % window length，N = 100 - 1000;Hamm = hamming(N); % create Hamming windowframe = input(请键入想要处理的帧位置 = );% origin is current frameorigin = Music_source(frame - 1) * (N / 2) + 1):(frame - 1) * (N / 2) + N);Frame = origin .* Hamm; %Short Time Fourier Transform%s1,f1,t1 = specgram(MusicSource,N,N/2,N); Xs1,Ys1 = size(s1);for i = 1:Xs1 FTframe1(i) = s1(i,frame);end N1 = input(请键入预测器阶数 = ); % N1 is predictors ordercoef,gain = lpc(Frame,N1); % LPC analysis using Levinson-Durbin recursionest_Frame = filter(0 -coef(2:end),1,Frame); % estimate frame(LP)FFT_est = fft(est_Frame);err = Frame - est_Frame; % error% FFT_err = fft(err);subplot(2,1,1),plot(1:N,Frame,1:N,est_Frame,-r);grid;title(原始语音帧vs.预测后语音帧)subplot(2,1,2),plot(err);grid;title(误差);pause %subplot(2,1,2),plot(f,20*log(abs(FTframe2);grid;title(短时谱) % Gain solution using G2 = Rn(0) - sum(ai*Rn(i),i = 1,2,.,P%fLength(1 : 2 * N) = origin,zeros(1,N); Xm = fft(fLength,2 * N);X = Xm .* conj(Xm);Y = fft(X , 2 * N);Rk = Y(1 : N);PART = sum(coef(2 : N1 + 1) .* Rk(1 : N1);G = sqrt(sum(Frame.2) - PART); A = (FTframe1 - FFT_est(1 : length(f1) ./ FTframe1 ; % inverse filter A(Z)subplot(2,1,1),plot(f1,20*log(abs(FTframe1),f1,(20*log(abs(1 ./ A),-r);grid;title(短时谱);subplot(2,1,2),plot(f1,(20*log(abs(G ./ A);grid;title(LPC谱);pause %plot(abs(ifft(FTframe1 ./ (G ./ A);grid;title(excited)%plot(f1,20*log(abs(FFT_est(1 : length(f1) .* A / G );grid;%pause % find_pitch%temp = FTframe1 - FFT_est(1 : length(f1); % not move higher frequnce pitch1 = log(abs(temp);pLength = length(pitch1);result1 = ifft(pitch1,N); % move higher frequncepitch1(pLength - 32) : pLength) = 0;result2 = ifft(pitch1,N); % direct do real cepstrum with errpitch = fftshift(rceps(err);origin_pitch = fftshift(rceps(Frame);subplot(211),plot(origin_pitch);grid;title(原始语音帧倒谱(直接调用函数);subplot(212),plot(pitch);grid;title(预测误差倒谱(直接调用函数);pause subplot(211),plot(1:length(result1),fftshift(real(result1);grid;title(预测误差倒谱(根据定义编写，没有去除高频分量);subplot(212),plot(1:length(result2),fftshift(real(result2);grid;title(预测误差倒谱(根据定义编写，去除高频分量);实验四 基于VQ的特定人孤立词语音识别研究1、mfcc.mfunction ccc = mfcc(x)bank=melbankm(24,256,8000,0,0.5,m);bank=full(bank);bank=bank/max(bank(:); for k=1:12 n=0:23; dctcoef(k,:)=cos(2*n+1)*k*pi/(2*24);end w = 1 + 6 * sin(pi * 1:12 ./ 12);w = w/max(w);xx=double(x);xx=filter(1 -0.9375,1,xx); xx=enframe(xx,256,80);for i=1:size(xx,1) y = xx(i,:); s = y .* hamming(256); t = abs(fft(s); t = t.2; c1=dctcoef * log(bank * t(1:129); c2 = c1.*w; m(i,:)=c2;end dtm = zeros(size(m);for i=3:size(m,1)-2 dtm(i,:) = -2*m(i-2,:) - m(i-1,:) + m(i+1,:) + 2*m(i+2,:);enddtm = dtm / 3; ccc = m dtm;ccc = ccc(3:size(m,1)-2,:);2、vad.mfunction x1,x2 = vad(x)x = double(x);x = x / max(abs(x); FrameLen = 240;FrameInc = 80; amp1 = 10;amp2 = 2;zcr1 = 10;zcr2 = 5; maxsilence = 8; % 6*10ms = 30msminlen = 15; % 15*10ms = 150msstatus = 0;count = 0;silence = 0; tmp1 = enframe(x(1:end-1), FrameLen, FrameInc);tmp2 = enframe(x(2:end) , FrameLen, FrameInc);signs = (tmp1.*tmp2)0.02;zcr = sum(signs.*diffs, 2);amp = sum(abs(enframe(filter(1 -0.9375, 1, x), FrameLen, FrameInc), 2);amp1 = min(amp1, max(amp)/4);amp2 = min(amp2, max(amp)/8);x1 = 0; x2 = 0;for n=1:length(zcr) goto = 0; switch status case 0,1 if amp(n) amp1 x1 = max(n-count-1,1); status = 2; silence = 0; count = count + 1; elseif amp(n) amp2 | . zcr(n) zcr2 status = 1; count = count + 1; else status = 0; count = 0; end case 2, if amp(n) amp2 | . zcr(n) zcr2 count = count + 1; else silence = silence+1; if silence maxsilence count = count + 1; elseif count minlen status = 0; silence = 0; count = 0; else status = 3; end end case 3, break; endend count = count-silence/2;x2 = x1 + count -1; 3、codebook.m%clear;function xchushi= codebook(m)a,b=size(m);m1,m2=szhixin(m);m3,m4=szhixin(m2);m1,m2=szhixin(m1);m7,m8=szhixin(m4);m5,m6=szhixin(m3);m3,m4=szhixin(m2);m1,m2=szhixin(m1);m15,m16=szhixin(m8);m13,m14=szhixin(m7);m11,m12=szhixin(m6);m9,m10=szhixin(m5);m7,m8=szhixin(m4);m5,m6=szhixin(m3);m3,m4=szhixin(m2);m1,m2=szhixin(m1);chushi(1,:)=zhixinf(m1);chushi(2,:)=zhixinf(m2);chushi(3,:)=zhixinf(m3);chushi(4,:)=zhixinf(m4);chushi(5,:)=zhixinf(m5);chushi(6,:)=zhixinf(m6);chushi(7,:)=zhixinf(m7);chushi(8,:)=zhixinf(m8);chushi(9,:)=zhixinf(m9);chushi(10,:)=zhixinf(m10);chushi(11,:)=zhixinf(m11);chushi(12,:)=zhixinf(m12);chushi(13,:)=zhixinf(m13);chushi(14,:)=zhixinf(m14);chushi(15,:)=zhixinf(m15);chushi(16,:)=zhixinf(m16);sumd=zeros(1,1000); k=1;dela=1;xchushi=chushi;while(k1 dela=abs(sumd(k)-sumd(k-1)/sumd(k);end k=k+1; chushi=xchushi;end return 4、testvq.m clear;disp(这是一个简易语音识别系统，请保证已经将您的语音保存在相应文件夹中)disp(正在训练您的语音模版指令，请稍后.)for i=1:10 fname = sprintf(D:matlabworkdtw1海儿的声音%da.wav,i-1); x = wavread(fname); x1 x2 = vad(x); m = mfcc(x); m = m(x1:x2-5,:); ref(i).code=codebook(m);enddisp(语音指令训练成功，恭喜！) disp(正在测试您的测试语音指令，请稍后.)for i=1:10fname = sprintf(D:matlabworkdtw1海儿的声音%db.wav,i-1); x = wavread(fname); x1 x2 = vad(x); mn = mfcc(x); mn = mn(x1:x2-5,:); %mn = mn(x1:x2,:) test(i).mfcc = mn;endsumsumdmax=0;sumsumdmin=0;disp(对训练过的语音进行测试)for w=1:10 sumd=zeros(1,10); a,b=size(test(w).mfcc);for i=1:10 for p=1:a for j=1:16 d(j)=odistan(test(w).mfcc(p,:),ref(i).code(j,:); end dmin=min(d); sumd(i)=sumd(i)+dmin;%endendsumdmin=min(sumd)/a;sumdmin1=min(sumd);sumdmax(w)=max(sumd)/a;sumsumdmin=sumdmin+sumsumdmax;sumsumdmax=sumdmax(w)+sumsumdmax; disp(正在匹配您的语音指令，请稍后.)for i=1:10 if (sumd(i)=sumdmin1) switch (i) case 1 fprintf(您输入的语音指令为:%s; 识别结果为%sn,前, 前); case 2 fprintf(您输入的语音指令为:%s; 识别结果为%sn,后, 后); case 3 fprintf(您输入的语音指令为:%s; 识别结果为%sn,左, 左); case 4 fprintf(您输入的语音指令为:%s; 识别结果为%sn,右, 右); case 5 fprintf(您输入的语音指令为:%s; 识别结果为%sn,东, 东); case 6 fprintf(您输入的语音指令为:%s; 识别结果为%sn,南, 南); case 7 fprintf(您输入的语音指令为:%s; 识别结果为%sn,西, 西); case 8 fprintf(您输入的语音指令为:%s; 识别结果为%sn,北, 北); case 9 fprintf(您输入的语音指令为:%s; 识别结果为%sn,上, 上); case 10 fprintf(您输入的语音指令为:%s; 识别结果为%sn,下, 下); otherwise fprintf(error); end endendenddelamin=sumsumdmin/10;delamax=sumsumdmax/10; disp(对没有训练过的语音进行测试) disp(正在测试你的语音，请稍后.)for i=1:10fname = sprintf(D:matlabworkdtw1%db.wav,i-1); x = wavread(fname); x1 x2 = vad(x); mn = mfcc(x); mn = mn(x1:x2-5,:); %mn = mn(x1: