语音信号处理实验报告集锦
【实验一】
一、实验题目
Short time analysis(短时间分析)
二、实验要求
Write a MA TLAB program to analyze a speech and simultaneously, on a single page, plot the following measurements:
1. the entire speech waveform
2. the short-time energy, En
3. the short-time magnitude, Mn
4. the short-time zero-crossing, Zn
5. the narrowband spectrogram
6. the wideband spectrogram
Use both the speech waveforms in the wznjdx_normal.wav. Choose appropriate window sizes, window shifts, and window for the analysis. Explain your choice of these parameters.
写一个程序来分析语音的同时，在一个单页，情节如下措施：
1。
整个语音波形
2。
短时间能量
3。
短时幅度，锰
4。
短时过零，锌
5。
窄带语谱图
6。
宽带语谱图
在wznjdx_normal.wav使用语音波形。
选择合适的窗口大小，窗口移动和窗口进行分析。
解释你对这些参数的选择。
三、实验程序
clear
[x,fs]=wavread('wznjdx_normal.wav');
n=length(x);
N=320;
subplot(4,1,1);plot(x);
h=linspace(1,1,N);
En=conv(h,x.*x);
subplot(4,1,2);plot(En);
Mn=conv(h,abs(x));
subplot(4,1,3);plot(Mn);
for i=1:n-1
if x(i)>=0 y(i)=1;
else y(i)=-1;
end
if x(i+1)>=0 y(i+1)=1;
else y(i+1)=-1;
end
w(i)=abs(y(i+1)-y(i));
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;
end
for w=1:j
Q(w)=Zm(160*(w-1)+1)/(2*N);
end
subplot(4,1,4);plot(Q);grid;
figure(2);
subplot(2,1,1);spectrogram(x,h,256,200,0.0424*fs); subplot(2,1,2);spectrogram(x,h,256,200,0.0064*fs);
四、实验结果
语谱图：（Matlab 7.0 用不了spectrogram）
【实验二】
一、实验题目
Homomorphic analysis同态分析
二、实验要求
Write a MATLAB program to compute the real cepstrums of a section of voiced speech and unvoiced speech.
Plot the signal, the log magnitude spectrum, the real cepstrum, and the lowpass liftered log magnitude spectrum.
写一个程序来计算实际倒谱一段浊音和清音。
小区的信号，对数幅度谱，真正的倒谱，和低通liftered对数幅度谱。
三、实验程序
nfft=256;
[x,fs] = wavread('wznjdx_normal.wav');
fx=x;
Xvm=log(abs(fft(fx,nfft)));
xhv=real(ifft(Xvm,nfft));
lifter=zeros(1,nfft);
lifter(1:30)=1;
lifter(nfft-28:nfft)=1;
fnlen=0.02*fs; % 20ms
win=hamming(fnlen);%加窗
n=fnlen;%窗宽度赋给循环自变量n noverlap=0.5*fnlen;
while(n<=length(x)-1)
fx=x(n-fnlen+1:n).*win;
n=n+noverlap;
end
xhvp=xhv.*lifter';
figure;
subplot(4,1,1)
plot(lifter);
title('倒谱滤波器');
subplot(4,1,2)
plot(x);
title('语音信号波形');
subplot(4,1,3)
plot(Xvm);
title('Xvm');
subplot(4,1,4)
plot(xhv);
title('xhv');
四、实验结果
【实验三】
一、实验题目
LP analysis线性分析
二、实验要求
Write a MATLAB program to convert from a frame of speech to a set of linear prediction coefficients. Plot the LPC spectrum superimposed on the corresponding STFT.
写一个程序将从语音到一组线性预测系数的框架。
情节的LPC谱叠加在相应的变换。
三、实验程序
clear;
[x,fs]=wavread('wznjdx_normal.wav');
fx=x(4000:4160-1);
p=10;
[a,e,k]=aryule(fx,p);
G=sqrt(e*length(fx));
f=log(abs(fft(fx)));
h0=zeros(1,160);
h=log(G)-log(abs(fft(a,160)));
figure(1);
subplot(211);plot(fx);
subplot(212);plot(f);
hold on;
plot((0:160-1),h,'r');
四、实验结果
【实验四】
一、实验题目
Pitch estimation
二、实验内容
Write a program to implement the pitch estimation and the voiced/unvoiced decision using the LPC-based method.
三、实验程序
clear
[x,fs]=wavread('wznjdx_normal.wav');
n=length(x);
Q = x';
NFFT=512;
N = 256;
Hamm = hamming(N);
frame = 30;
M = Q(((frame -1) * (N / 2) + 1):((frame - 1) * (N / 2) + N)); Frame = M .* Hamm';
% lowpass filter
[b2,a2]=butter(2,900/4000);
speech2=filter(b2,a2,Frame); % filter
% residual
[a,e] = lpc(speech2,20);
errorlp=filter(a,1,speech2); % residual
% Short-term autocorrelation.
re = xcorr(errorlp);
% Find max autocorrelation for lags in the interval minlag to maxlag. minlag = 17; % F0: 450Hz
maxlag =160; % F0: 50Hz
fnlen=256;
[remax,idx] = max(re(fnlen+minlag:fnlen+maxlag));
figure
subplot(3,1,1);plot(Frame);
subplot(3,1,2);plot(speech2);
subplot(3,1,3);plot(re);text(500,0,'idx');
idx=idx-1+minlag
remax
四、实验结果
【实验五】
一、实验题目
Speech synthesis语音合成
二、实验内容
Write a program to analyze a speech and synthesize it using the LPC-based method. 写一个程序来分析语音和综合使用LPC法。
三、实验程序
主程序
clear;
[x,sr] = wavread('wznjdx_normal.wav');
p=[1 -0.9];
x=filter(p,1,x);
N=256;
inc=128;
y=lpcsyn(x,N,inc);
wavplay(y,sr);
子程序lpcsyn
function y=lpcsyn(x,N,inc)
%[x,sr] = wavread('wznjdx_normal.wav');
%pre = [1 -0.97];
%x = filter(pre,1,x);
%N=256;
%inc=128;
fn=floor(length(x)/inc);
y=zeros(1,50000);
for (i=1:fn)
order=16;
x(1:N,i)=x((i-1)*inc+1:(i+1)*inc);
[A(i,:),G(i),P(i),fnlen,fnshift] = lpcana(x(1:N,i),order);
if (P(i)) % V oiced frame.
e = zeros(N,1);
e(1:P(i):N) = 1; % Impulse-train excitation.
else % Unvoiced frame.
e = randn(N,1); % White noise excitation. end
yt=filter(G(i),A(i,:),e);
j=(i-1)*inc+[1:N];
y(j) = y(j)+yt';
end;
end
子程序lpcana
function [A,G,P,fnlen,fnshift] = lpcana(x,order)
fnlen=256;
fnshift=fnlen/2;
n=length(x);
[b2,a2]=butter(2,900/4000);
speech2=filter(b2,a2,x);
[A,e]=lpc(speech2,order);
errorlp=filter(A,1,speech2);
re=xcorr(errorlp);
G=sqrt(e*length(speech2));
minlag=17;
maxlag=160;
[remax,idx]=max(re(n+minlag:n+maxlag));
P=idx-1+minlag;
end
四、实验结果
【实验六】
一、实验题目
Speech enhancement语音增强
二、实验内容
Write a program to implement the basic spectral magnitude subtraction. 编写一个程序，实现基本的谱幅度减法。
三、实验程序
clear
[speech,fs,nbits]=wavread('wznjdx_normal.wav');%读入数据
alpha=0.04;%噪声水平
winsize=256;%窗长
size=length(speech);%语音长度
numofwin=floor(size/winsize);%帧数
hamwin=zeros(1,size);%定义汉明窗长度
enhanced=zeros(1,size);%定义增强语音的长度
ham=hamming(winsize)';%%产生汉明窗
x=speech'+alpha*randn(1,size);%信号加噪声
noisy=alpha*randn(1,winsize);%噪声估计
N=fft(noisy);
nmag=abs(N);%噪声功率谱
%分帧
for q=1:2*numofwin-1
frame=x(1+(q-1)*winsize/2:winsize+(q-1)*winsize/2);%对带噪语音帧间重叠一半取值hamwin(1+(q-1)*winsize/2:winsize+(q-1)*winsize/2)=...
hamwin(1+(q-1)*winsize/2:winsize+(q-1)*winsize/2)+ham;%加窗y=fft(frame.*ham);
mag=abs(y);%带噪语音功率谱
phase=angle(y);%带噪语音相位
%幅度谱减
for i=1:winsize
if mag(i)-nmag(i)>0
clean(i)=mag(i)-nmag(i);
else clean(i)=0;
end
end
%频域中重新合成语音
spectral=clean.*exp(j*phase);
%反傅里叶变换并重叠相加
enhanced(1+(q-1)*winsize/2:winsize+(q-1)*winsize/2)=...
enhanced(1+(q-1)*winsize/2:winsize+(q-1)*winsize/2)+real(ifft(spectral));
end
figure(1);
subplot(3,1,1);plot(speech);xlabel('样点数');ylabel('幅度');title('原始语音波形');
subplot(3,1,2);plot(x);xlabel('样点数');ylabel('幅度');title('语音加噪波形');
subplot(3,1,3);plot(enhanced);xlabel('样点数');ylabel('幅度');title('增强语音波形');
四、实验结果。
