voice_wav_frequency.zip_speech frequency_语音时域特征_降噪_频信号检测_频域滤波

N = 320; [Y,fspeed,nbits] = wavread('SA1.wav'); read_speed=15000;%声音播放速率 %sound(Y,read_speed);%声音播放 L = length(Y);%30秒，每秒8000个点，一共240000个点 LL = length(Y)/N; %一共 1000帧 figure(5); plot(Y); title('原始语音信号-design for QQ 明天过后') xlabel('样点数');ylabel('幅度'); figure(7); [x1,fs,bits]=wavread('SA1.wav'); y1=fft(x1,1024); f=fs*(0:511)/1024; plot(f,abs(y1(1:512))); title('原始语音信号频谱-design for QQ 明天过后'); figure(6); [x1,fs,bits]=wavread('SA1.wav'); y1=fft(x1,1024); f=fs*(0:511)/1024; plot(abs(y1(1:1024))); title('采样后语音信号频谱-design for QQ 明天过后'); figure(1); subplot(311); plot(Y); title('原始语音信号') xlabel('样点数');ylabel('幅度'); %短时能量 Em=zeros(1,(LL-1)*N); for ii=1:(LL-1)*N temp=Y(ii:ii+N); Em(ii)=sum(temp.*temp); end % axis([12000,16000,0.15,0.4]) %短时平均幅度Mn=sum(abs(Y))/N Mn=zeros(1,(LL-1)*N); for ii=1:(LL-1)*N temp=Y(ii:ii+N); Mn(ii)=sum(abs(temp))/N; end subplot(312); jj=[1:(LL-1)*N]; plot(jj, Em,'b'); xlabel('样点数');ylabel('短时能量E'); subplot(313); jj=[1:(LL-1)*N]; plot(jj, Mn,'b'); xlabel('样点数');ylabel('短时平均幅度M'); %短时过零率 Zn=zeros(1,(LL-1)*N); for ii=2:(LL-1)*N temp1=sign(Y(ii:ii+N)); temp=sign(Y(ii-1:ii+N-1)); Zn(ii)=sum(abs(temp1-temp)); end figure(2) subplot(211); plot(Y); title('原始语音信号') xlabel('样点数');ylabel('幅度'); subplot(212) jj=[1:(LL-1)*N]; plot(jj, Zn,'b'); title('原始信号的过零率') xlabel('样点数');ylabel('过零次数'); grid % axis([12000,16000,0,20]) %%%%%%浊音，取13270--13510个点 %短时自相关函数 temp=Y(21200:21520); % % wc1 = 0.0075; wc2 = 0.9625; % wn = [wc1/pi,wc2/pi]; % h = fir1(239,wn,blackman(240)); % h=reshape(h,240,1); % temp= temp.*abs(h); % %% %temp2=reshape(temp,1,240); %temp=temp2.*B; Rn1=zeros(1,N); for nn=[1:N], for ii=[1:N-nn], Rn1(nn) =Rn1(nn)+ temp(ii)*temp(nn+ii); end end figure(3) subplot(2,1,1) jj=[1:N]; plot(jj, Rn1,'b'); title('浊音信号自相关函数') xlabel('延时k');ylabel('R（k）'); grid %axis([9,50,-0.01,0.02]) %短时平均幅度差函数 Yn1=zeros(1,N); for nn=[1:N], for ii=[1:N-nn], Yn1(nn) =Yn1(nn)+ abs(temp(ii)-temp(nn+ii)); end end figure(4) subplot(2,1,1) jj=[1:N]; plot(jj, Yn1,'b'); title('浊音信号平均幅度差') xlabel('延时k');ylabel('R（k）'); grid %%%%%%清音，取12120--12360个点 %短时自相关函数 temp=Y(52200:52520); %% %temp2=reshape(temp,1,240); %temp=temp2.*B; Rn2=zeros(1,N); for nn=[1:N], for ii=[1:N-nn], Rn2(nn) =Rn2(nn)+ temp(ii)*temp(nn+ii); end end figure(3) subplot(212) jj=[1:N]; plot(jj, Rn2,'b'); title('清音信号自相关函数') xlabel('延时k');ylabel('R（k）'); grid %axis([9,50,-0.01,0.02]) %短时平均幅度差函数 Yn2=zeros(1,N); for nn=[1:N], for ii=[1:N-nn], Yn2(nn) =Yn2(nn)+ abs(temp(ii)-temp(nn+ii)); end end figure(4) subplot(212) jj=[1:N]; plot(jj, Yn2,'b'); title('清音信号平均幅度差') xlabel('延时k');ylabel('R（k）'); grid %加噪声 x0=wavread('SA1.wav'); x=x0(:,1)%单声道 x1=x'; z=0.03*randn(1,length(x1));%加噪声 x2=x1+z; y2=fft(x2,3000); %sound(x2,read_speed);%噪声淹没原始信号 figure(8); subplot(211); plot(x2); grid on; axis tight; title('原始语音信号加入噪声时域波形-design for QQ 明天过后') xlabel('time（s）');ylabel('幅度'); subplot(212); plot(abs(y2(1:3000))); grid on; axis tight; title('原始语音信号加入噪声频域波形-design for QQ 明天过后') xlabel('Hz');ylabel('幅值'); %降噪滤波 %巴特沃斯低通滤波器 %fs=5000; %x1=wavread('SA1.wav'); %t=0:1/50000:(size(x1)-1)/5000; %Au=0.03; %d=[Au*cos(2*pi*5000*t)]'; %x2=x1+d; %wp=0.25*pi; %ws=0.3*pi; wp=0.20*pi; ws=0.25*pi; Rp=1; Rs=15;%15 Fs=5000; Ts=1/Fs; wp1=2/Ts*tan(wp/2);%模拟指标转换为数字指标 ws1=2/Ts*tan(ws/2); [N,Wn]=buttord(wp1,ws1,Rp,Rs,'s');%选择滤波器的最小阶数 [Z,P,K]=buttap(N);%创建巴特沃斯模拟低通滤波器 [Bap,Aap]=zp2tf(Z,P,K); [b,a]=lp2lp(Bap,Aap,Wn); [bz,az]=bilinear(b,a,Fs);%双线性变换到数字滤波器 f1=filter(bz,az,x2); figure(9); subplot(211); plot(f1); title('噪声滤波后时域波形-design for QQ 明天过后') xlabel('time（s）');ylabel('幅度'); %sound(f1,read_speed);%降噪 f2=fft(f1,3000); subplot(212); plot(abs(f2(1:3000))); grid on; axis tight; title('噪声滤波后频域波形-design for QQ 明天过后') xlabel('Hz');ylabel('幅值');