語(yǔ)音信號(hào)處理

實(shí)驗(yàn)四 減譜法語(yǔ)音增強(qiáng)技術(shù)研究一、實(shí)驗(yàn)?zāi)康谋緦?shí)驗(yàn)要求掌握減譜法語(yǔ)音增強(qiáng)的原理，會(huì)利用已學(xué)的相關(guān)語(yǔ)音特征，構(gòu)建語(yǔ)音特征矢量，然后自己設(shè)計(jì)減譜法語(yǔ)音增強(qiáng)程序（也可參考相關(guān)文獻(xiàn)），能顯示干凈語(yǔ)音和加噪語(yǔ)音信號(hào)及處理后的結(jié)果語(yǔ)音信號(hào)波形，分析實(shí)驗(yàn)結(jié)果，寫出報(bào)告。二、實(shí)驗(yàn)原理譜減法的基本原理譜相減方法是基于人的感覺(jué)特性，即語(yǔ)音信號(hào)的短時(shí)幅度比短時(shí)相位更容易對(duì)人的聽覺(jué)系統(tǒng)產(chǎn)生影響，從而對(duì)語(yǔ)音短時(shí)幅度譜進(jìn)行估計(jì)，適用于受加性噪聲污染的語(yǔ)音。由于語(yǔ)音是短時(shí)平穩(wěn)的，所以在短時(shí)譜幅度估計(jì)中認(rèn)為它是平穩(wěn)隨機(jī)信號(hào)，假設(shè)、和分別代表語(yǔ)音、噪聲和帶噪語(yǔ)音，、和分別表示其短時(shí)譜。假設(shè)噪聲是與語(yǔ)音不相關(guān)的加性噪聲。于是得到信號(hào)的加性模型： (4-1)對(duì)功率譜有 (4-4)原始語(yǔ)音的估值為 (4-5)只要在頻域用(4-5)式得到純凈語(yǔ)音的譜估計(jì)，就可以根據(jù)(4-6)式得到增強(qiáng)后的語(yǔ)音。 (4-6)根據(jù)前面分析，我們可以給出譜相減算法的整個(gè)算法流程，如圖4-1所示：增強(qiáng)后的語(yǔ)音帶噪語(yǔ)音啊FFTIFFT相位信息減去估計(jì)噪聲圖4-1 譜減法的算法流程三、實(shí)驗(yàn)程序1、噪聲疊加到信號(hào)上的比較%在噪聲環(huán)境下語(yǔ)音信號(hào)的增強(qiáng)%語(yǔ)音信號(hào)為讀入的聲音文件%噪聲為正態(tài)隨機(jī)噪聲clear;input=wavread(C:UsersAdministratorDesktopyuyinxinhaob1.wav);count=length(input);noise1=0.1*randn(1,count);signal=input;for i=1:countvoice1(i)=signal(i)+noise1(i);endnoise2=0.01*randn(1,count);for i=1:countvoice2(i)=signal(i)+noise2(i);endnoise3=randn(1,count);signal=input;for i=1:countvoice3(i)=signal(i)+noise3(i);endn=1:count;figure %對(duì)比純凈語(yǔ)音信號(hào)，噪音信號(hào)和帶噪語(yǔ)音信號(hào)subplot(3,1,1);plot(n,signal);title(純凈信號(hào))subplot(3,1,2);plot(n,noise1);title(噪音信號(hào))subplot(3,1,3);plot(n,voice1);title(帶噪信號(hào))figure %對(duì)比純凈語(yǔ)音信號(hào)頻譜，噪音信號(hào)和帶噪信號(hào)頻譜Fss=fft(signal);subplot(3,1,1);plot(n,abs(Fss);title(純凈信號(hào)頻譜)Fss1=fft(noise1);subplot(3,1,2);plot(n,abs(Fss1);title(噪音信號(hào)頻譜)Fv1=fft(voice1);subplot(3,1,3)plot(n,abs(Fv1);title(帶噪信號(hào)的頻譜)figure %對(duì)比純凈語(yǔ)音信號(hào)，噪音信號(hào)和帶噪語(yǔ)音信號(hào)subplot(3,1,1);plot(n,signal);title(純凈信號(hào))subplot(3,1,2);plot(n,noise2);title(噪音信號(hào))subplot(3,1,3);plot(n,voice2);title(帶噪信號(hào))figure %對(duì)比純凈語(yǔ)音信號(hào)頻譜，噪音信號(hào)和帶噪信號(hào)頻譜Fss=fft(signal);subplot(3,1,1);plot(n,abs(Fss);title(純凈信號(hào)頻譜)Fss2=fft(noise2);subplot(3,1,2);plot(n,abs(Fss2);title(噪音信號(hào)頻譜)Fv2=fft(voice2);subplot(3,1,3)plot(n,abs(Fv2);title(帶噪信號(hào)的頻譜)figure %對(duì)比純凈語(yǔ)音信號(hào)，噪音信號(hào)和帶噪語(yǔ)音信號(hào)subplot(3,1,1);plot(n,signal);title(純凈信號(hào))subplot(3,1,2);plot(n,noise3);title(噪音信號(hào))subplot(3,1,3);plot(n,voice3);title(帶噪信號(hào))figure %對(duì)比純凈語(yǔ)音信號(hào)頻譜，噪音信號(hào)和帶噪信號(hào)頻譜Fss=fft(signal);subplot(3,1,1);plot(n,abs(Fss);title(純凈信號(hào)頻譜)Fss3=fft(noise3);subplot(3,1,2);plot(n,abs(Fss3);title(噪音信號(hào)頻譜)Fv3=fft(voice3);subplot(3,1,3)plot(n,abs(Fv3);title(帶噪信號(hào)的頻譜)2、利用減譜法的基本原理給語(yǔ)音信號(hào)降噪 噪聲為0.1*randn(1,coun) 純凈信號(hào)為輸入信號(hào)%在噪聲環(huán)境下語(yǔ)音信號(hào)的增強(qiáng)%語(yǔ)音信號(hào)為讀入的聲音文件%噪聲為正態(tài)隨機(jī)噪聲clear;input=wavread(C:UsersAdministratorDesktopyuyinxinhaob1.wav);count=length(input);noise=1*randn(1,count);signal=input;for i=1:countvoice(i)=signal(i)+noise(i);endFv=fft(voice);anglev=angle(Fv);Fn=fft(noise);power1=(abs(Fv).2;power2=(abs(Fn).2;power3=power1-power2;power4=sqrt(power3);Fs=power4.*exp(j*anglev);sound=ifft(Fs);n=1:count;%純凈語(yǔ)音信號(hào)頻譜Fss=fft(signal);figure %對(duì)比純凈語(yǔ)音信號(hào)和輸出信號(hào)subplot(2,1,1)plot(n,signal);title(純凈信號(hào))subplot(2,1,2)plot(n,sound);title(輸出信號(hào))figure %對(duì)比純凈語(yǔ)音信號(hào)頻譜和輸出語(yǔ)音信號(hào)頻譜subplot(2,1,1)plot(n,abs(Fss);title(純凈信號(hào)頻譜)subplot(2,1,2)plot(n,abs(Fs);title(輸出信號(hào)頻譜)max_v=max(voice); %對(duì)帶噪信號(hào)抽樣值點(diǎn)進(jìn)行歸一化處理re_voice=voice/max_v;%對(duì)輸出信號(hào)抽樣點(diǎn)值進(jìn)行歸一化處理max_s=max(sound);re_sound=sound/max_s;%讀出帶噪語(yǔ)音信號(hào)，存為1001.wavwavwrite(re_voice,5500,16,1001);%讀出處理后語(yǔ)音信號(hào)，存為1002.wavwavwrite(re_sound,5500,16,1002)3、利用改進(jìn)的減譜法給語(yǔ)音信號(hào)降噪噪聲為0.1*randn(1,coun) 純凈信號(hào)為輸入信號(hào)%在噪聲環(huán)境下語(yǔ)音信號(hào)的增強(qiáng)%語(yǔ)音信號(hào)為讀入的聲音文件%噪聲為正態(tài)隨機(jī)噪聲clear;input=wavread(C:UsersAdministratorDesktopyuyinxinhaob1.wav);count=length(input);noise=0.1*randn(1,count);signal=input;for i=1:countvoice(i)=signal(i)+noise(i);endFv=fft(voice);anglev=angle(Fv);Fn=fft(noise);power1=(abs(Fv).2;power2=(abs(Fn).2;for i=1:count if(power1(i)=3*power2(i) power3(i)=power1(i)-3*power2(i); else power3(i)=0.01*power2(i); endendpower4=sqrt(power3);Fs=power4.*exp(j*anglev);sound=ifft(Fs);n=1:count;%純凈語(yǔ)音信號(hào)頻譜Fss=fft(signal);figure %對(duì)比純凈語(yǔ)音信號(hào)和輸出信號(hào)subplot(2,1,1)plot(n,signal);title(純凈信號(hào))subplot(2,1,2)plot(n,sound);title(輸出信號(hào))figure %對(duì)比純凈語(yǔ)音信號(hào)頻譜和輸出語(yǔ)音信號(hào)頻譜subplot(2,1,1)plot(n,abs(Fss);title(純凈信號(hào)頻譜)subplot(2,1,2)plot(n,abs(Fs);title(輸出信號(hào)頻譜)四、實(shí)驗(yàn)結(jié)果1、噪聲疊加到信號(hào)上的比較（1）噪聲為0.1*randn(1,count)（2）噪聲為0.01*randn(1,count)（3）噪聲為randn(1,count)2、利用減譜法的基本原理給語(yǔ)音信號(hào)降噪噪聲為0.1*randn(1,coun) 純凈信號(hào)為輸入信號(hào)3、利用改進(jìn)的減譜法給語(yǔ)音信號(hào)降噪噪聲為0.1*randn(1,coun) 純凈信號(hào)為輸入信號(hào)（1）參數(shù)取a=3,b=0.01（2）參數(shù)取a=10,b=0.01（3）參數(shù)取a=0.8,b=0.01（4）參數(shù)取a=3,b=0.0001（5）參數(shù)取a=3,b=0.1五、實(shí)驗(yàn)分析1、噪聲疊加到信號(hào)上時(shí)，噪聲會(huì)對(duì)信號(hào)產(chǎn)生較大影響，噪聲加強(qiáng)會(huì)使信號(hào)被噪聲淹沒(méi)；噪聲減弱，對(duì)信號(hào)的影響減小;2、用減譜法的基本原理対帶噪信號(hào)進(jìn)行處理后，信號(hào)質(zhì)量明顯提升，從而得到較純凈的語(yǔ)音信號(hào);3、用改進(jìn)的減譜法対帶噪信號(hào)進(jìn)行處理后，信號(hào)質(zhì)量更好，語(yǔ)音信號(hào)更純凈。合理選擇參數(shù)a、b很重要，由于實(shí)驗(yàn)所用的是輔音，去a=3,b=0.01左右較合適。a太大或太小，不能取得較好的降噪效果,b太小，不能抑制純音噪聲，b太大會(huì)造成噪聲，必須b1。六、實(shí)驗(yàn)總結(jié)通過(guò)本次實(shí)驗(yàn)，我知道了：減譜法作為語(yǔ)音增強(qiáng)技術(shù)的有效方法之一,能夠有效地降低包含在語(yǔ)音中的加性噪聲,但是由于減譜法針對(duì)的是寬帶噪聲較為