小波去噪软阈值和硬阈值的matlab仿真程序.doc

.%设置信噪比和随机种子值snr=4;init=2055615866;%产生原始信号sref和高斯白噪声污染的信号ssref,s=wnoise(1,11,snr,init);%用db1小波对原始信号进行3层分解并提取系数c,l=wavedec(s,3,db1);a3=appcoef(c,l,db1,3);d3=detcoef(c,l,3);d2=detcoef(c,l,2);d1=detcoef(c,l,1);thr=1;%进行硬阈值处理ythard1=wthresh(d1,h,thr);ythard2=wthresh(d2,h,thr);ythard3=wthresh(d3,h,thr);c2=a3 ythard3 ythard2 ythard1;s3=waverec(c2,l,db1);%进行软阈值处理ytsoftd1=wthresh(d1,s,thr);ytsoftd2=wthresh(d2,s,thr);ytsoftd3=wthresh(d3,s,thr);c3=a3 ytsoftd3 ytsoftd2 ytsoftd1;s4=waverec(c3,l,db1);%对上述信号进行图示subplot(5,1,1);plot(sref);title(参考信号);subplot(5,1,2);plot(s);title(染噪信号);subplot(5,1,3);plot(s3);title(硬阈值处理);subplot(5,1,4);plot(s4);title(软阈值处理);%设置信噪比和随机种子值snr=4;init=2055615866;%产生原始信号sref和高斯白噪声污染的信号ssref,s=wnoise(1,11,snr,init);%用db1小波对原始信号进行3层分解并提取系数c,l=wavedec(s,3,db1);a3=appcoef(c,l,db1,3);d3=detcoef(c,l,3);d2=detcoef(c,l,2);d1=detcoef(c,l,1);thr=1;%进行硬阈值处理ythard1=wthresh(d1,h,thr);ythard2=wthresh(d2,h,thr);ythard3=wthresh(d3,h,thr);c2=a3 ythard3 ythard2 ythard1;s3=waverec(c2,l,db1);%进行软阈值处理ytsoftd1=wthresh(d1,s,thr);ytsoftd2=wthresh(d2,s,thr);ytsoftd3=wthresh(d3,s,thr);c3=a3 ytsoftd3 ytsoftd2 ytsoftd1;s4=waverec(c3,l,db1);%对上述信号进行图示subplot(5,1,1);plot(sref);title(参考信号);subplot(5,1,2);plot(s);title(染噪信号);subplot(5,1,3);plot(s3);title(硬阈值处理);subplot(5,1,4);plot(s4);title(软阈值处理);Matlab小波消噪程序（原创）Yo,FS,NBITS,OPTS=wavread(normal_s3.wav);fs=FSY=Yo;dt=1/FS;YY=Yo(1800:9500);N=length(YY);Y=YY+0.14*randn(1,N);time=(0:N-1)*dt;(N-1)*dtfY=fs*(1:N/2)/N;yYY=abs(fft(YY);yY=abs(fft(Y);c,l=wavedec(Y,4,coif4);a4=appcoef(c,l,coif4,4);a3=appcoef(c,l,coif4,3);a2=appcoef(c,l,coif4,2);a1=appcoef(c,l,coif4,1);d4=detcoef(c,l,4);d3=detcoef(c,l,3);d2=detcoef(c,l,2);d1=detcoef(c,l,1);figure(1)subplot(511);plot(a4);title(a4);subplot(512);plot(d4);title(d4);subplot(513);plot(d3);title(d3);subplot(514);plot(d2);title(d2);subplot(5,1,5);plot(d1);title(d1);SNRin=10*log(norm(YY)2/(norm(Y-YY)2) %去噪前信号的信噪比figure(2)subplot(211);plot(time,YY);axis(0 time(end) -1.5 1.5);xlabel(time/s);title(原音信号);subplot(212);plot(fY,yYY(1:floor(N/2);xlabel(f/hz);ylabel(频谱);title(原心音信号频谱图);xlim(0 500)figure(3)subplot(211);plot(time,Y);axis(0 time(end) -1.5 1.5);xlabel(time/s);title(加噪心音信号);subplot(212);plot(fY,yY(1:floor(N/2);xlabel(f/hz);ylabel(频谱);title(加噪心音信号频谱);xlim(0 500)%单级阈值去噪%thr=yuzhi(Y,4,coif4,sqtwolog,sln)dd1=yuzhifunction(d1,thr(1),soft);dd2=yuzhifunction(d2,thr(2),soft);dd3=yuzhifunction(d3,thr(3),soft);dd4=yuzhifunction(d4,thr(4),soft);cl=a4,dd4,dd3,dd2,dd1;s=waverec(cl,l,coif4);N1=length(s);times=(0:N1-1)*dt;fs0=fs*(0:N1/2-1)/N1;ys=abs(fft(s);%多级阈值去噪%thr1=yuzhi(Y,4,coif4,sqtwolog,mln)s1,cxc,clc,perf2,perf3=wdencmp(lvd,Y,coif4,4,thr1,s);N2=length(s1);times1=(0:N2-1)*dt;fs1=fs*(1:N2/2)/N2;ys1=abs(fft(s1);figure(4);subplot(321);plot(time,YY);axis(0 time(end) -1.5 1.5);xlabel(time/s);title(原音信号);subplot(322);plot(fY,yYY(1:floor(N/2);xlabel(f/hz);ylabel(频谱);title(原心音信号频谱图);xlim(0 400)subplot(323);plot(times,s);axis(0 time(end) -1.5 1.5);xlabel(time/s);title(单级阈值去噪心音信号);subplot(324);plot(fs0,ys(1:floor(N1/2);xlabel(f/hz);ylabel(频谱);title(单级阈值去噪心音信号频谱图);xlim(0 400)subplot(325);plot(times1,s1);axis(0 time(end) -1.5 1.5);xlabel(time/s);title(多级阈值去噪心音信号);subplot(326);plot(fs1,ys1(1:floor(N2/2);xlabel(f/hz);ylabel(频谱);title(多级阈值去噪心音信号频谱图);xlim(0 400)想把这个程序。改为只用软硬阈值对比的心电信号去噪分析，该怎么办？ 求高手。程序见下：%信号小波分解%基于Haar小波%ecg=fopen(100.dat,r);N=1201;data=fread(ecg,N,int16);data=data/10000;save ECGdata data;fclose(ecg);x=0:0.004:4.8;figure(1);subplot(321);plot(x,data);axis(0 4.8 -4 4);title(原始心电信号);x=data;wname=Haar;level=5;c,l=wavedec(x,level,wname);a5=wrcoef(a,c,l,Haar,5);a4=wrcoef(a,c,l,Haar,4);a3=wrcoef(a,c,l,Haar,3);a2=wrcoef(a,c,l,Haar,2);a1=wrcoef(a,c,l,Haar,1);subplot(322);plot(a5);title(a5);axis(0 1201 -2 2);subplot(323);plot(a4);title(a4);axis(0 1201 -2 2);subplot(324);plot(a3);title(a3);axis(0 1201 -2 2);subplot(325);plot(a2);title(a2);axis(0 1201 -2 2);subplot(326);plot(a1);title(a1);axis(0 1201 -2 2);%基于db6小波%ecg=fopen(100.dat,r);N=1201;data=fread(ecg,N,int16);data=data/10;fclose(ecg);x=0:0.004:4.8;figure(2);subplot(321);plot(x,data);axis(0 4.8 -4000 4000);title(原始心电信号);x=data;wname=db6;level=5;c,l=wavedec(x,level,wname);a5=wrcoef(a,c,l,db6,5);a4=wrcoef(a,c,l,db6,4);a3=wrcoef(a,c,l,db6,3);a2=wrcoef(a,c,l,db6,2);a1=wrcoef(a,c,l,db6,1);subplot(322);plot(a5);title(a5);axis(0 1201 -2000 2000);subplot(323);plot(a4);title(a4);axis(0 1201 -2000 2000);subplot(324);plot(a3);title(a3);axis(0 1201 -2000 2000);subplot(325);plot(a2);title(a2);axis(0 1201 -2000 2000);subplot(326);plot(a1);title(a1);axis(0 1201 -2000 2000);%基于sym3小波%ecg=fopen(100.dat,r);N=1201;data=fread(ecg,N,int16);data=data/10;save ECGdata data;fclose(ecg);x=0:0.004:4.8;figure(3);subplot(321);plot(x,data);axis(0 4.8 -4000 4000);title(原始心电信号);x=data;wname=sym3;level=5;c,l=wavedec(x,level,wname);a5=wrcoef(a,c,l,sym3,5);a4=wrcoef(a,c,l,sym3,4);a3=wrcoef(a,c,l,sym3,3);a2=wrcoef(a,c,l,sym3,2);a1=wrcoef(a,c,l,sym3,1);subplot(322);plot(a5);title(a5);axis(0 1201 -2000 2000);subplot(323);plot(a4);title(a4);axis(0 1201 -2000 2000);subplot(324);plot(a3);title(a3);axis(0 1201 -2000 2000);subplot(325);plot(a2);title(a2);axis(0 1201 -2000 2000);subplot(326);plot(a1);title(a1);axis(0 1201 -2000 2000);%心电信号降噪%Birge-Massart策略阈值降噪%基于小波变换的心电信号的降噪ecg=fopen(100.dat,r);N=1201;data=fread(ecg,N,int16);data=data/10000;fclose(ecg);x=data;wavename=db5;level=4;c,l=wavedec(x,level,wavename);alpha=1.5;sorh=h;thr,nkeep=wdcbm(c,l,alpha);%使用硬阈值给信号降噪xc,cxc,lxc,perf0,perfl2=wdencmp(lvd,c,l,wavename,level,thr,sorh);t1=0:0.004:(length(x)-1)*0.004;figure(4);subplot(211);plot(t1,x);title(从人体采集的原始的ECG信号);subplot(212);plot(t1,xc);title(Birge-Massart策略阈值降噪后的ECG信号（wname=db5 level=4）);%最优预测软阈值降噪%基于小波变换的心电信号的压缩ecg=fopen(100.dat,r);N=1201;data=fread(ecg,N,int16);data=data/10000;fclose(ecg);x=data;wavename=db3;level=4;xd,cxd,lxd=wden(x,heursure,s,mln,level,wavename);t=0:0.004:4.8;figure(5);subplot(311);plot(t,x);title(从人体采集的原始的ECG信号);subplot(312);plot(t,xd);title(heursure规则阈值降噪后的ECG信号(db3 level=4);%最小均方差软阈值降噪%基于小波变换的心电信号的压缩ecg=fopen(100.dat,r);N=1201;data=fread(ecg,N,int16);data=data/10000;fclose(ecg);x=data;wavename=db3;level=4;xd=wden(x,minimaxi,s,mln,level,wavename);t=0:0.004:4.8;figure(6);subplot(311);plot(t,x);title(从人体采集的原始的ECG信号);subplot(313);plot(t,xd);title(Minimax规则阈值降噪后的ECG信号(wname=db3 level=4);%心电信号压缩%基于小波变换的心电信号的压缩ecg=fopen(100.dat,r);N=1201;data=fread(ecg,N,int16);data=data/10000;fclose(ecg);x=data;wavename=db5;c,l=wavedec(x,4,wavename);alpha=2;sorh=h;thr,nkeep=wdcbm(c,l,alpha);%使用硬阈值压缩信号xc,cxc,lxc,perf0,perfl2=wdencmp(lvd,c,l,wavename,4,thr,sorh);t=0:0.004:4.8;figure(7);subplot(511);plot(t,x);title(原始的ECG信号);axis(0 5 -3 3);subplot(512);plot(t,xc);axis(0 5 -3 3);title(在第4层对高频系数量化压缩后的ECG信号（alpha=2 db5）);c,l=wavedec(x,4,wavename);alpha=4;sorh=h;thr,nkeep=wdcbm(c,l,alpha);%使用硬阈值压缩信号xc1,cxc1,lxc1,perf01,perfl21=wdencmp(lvd,c,l,wavename,4,thr,sorh);subplot(513);plot(t,xc1);axis(0 5 -3 3);title(在第4层对高频系数量化压缩后的ECG信号alpha=4 db5);c,l=wavedec(x,2,wavename);alpha=4;sorh=h;thr,nkeep=wdcbm(c,l,alpha);%使用硬阈值压缩信号xc1,cxc1,lxc1,perf01,perfl21=wdencmp(lvd,c,l,wavename,2,thr,sorh);subplot(514);plot(t,xc1);axis(0 5 -3 3);title(在第2层对高频系数量化压缩后的ECG信号alpha=4 db5);c,l=wavedec(x,2,db3);alpha=4;sorh=h;thr,nkeep=wdcbm(c,l,alpha);%使用硬阈值压缩信号xc1,cxc1,lxc1,perf01,perfl21=wdencmp(lvd,c,l,db3,2,thr,sorh);subplot(515);plot(t,xc1);axis(0 5 -3 3);title(在第2层对高频系数量化压缩后的ECG信号alpha=4 db3);%Matlab小波去噪（默认，强制，给定三种情况）% 利用小波分析对监测采集的信号进行去噪处理，恢复原始信号%小波分析进行去噪有3中方法：%1、默认阈值去噪处理。该方法利用函数ddencmp( )生成信号的默认阈值，然后利用函数wdencmp( )进行去噪处理；%2、给定阈值去噪处理。在实际的去噪处理过程中，阈值往往可通过经验公式获得，且这种阈值比默认阈值的可信度高。在进行阈值量化处理时可利用函数wthresh( );%3、强制去噪处理。该方法是将小波分解结构中的高频系数全部置0，即滤掉所有高频部分，然后对信号进行小波重构。这种方法比较简单，且去噪后的信号比较平滑，但是容易丢失信号中的有用成分。% 利用小波分析对监测采集的水轮机信号进行去噪处理，恢复原始信号%Program Start% 载入监测所得信号load default.txt; %装载采集的信号x= default;lx=length(x);t=0:1:length(x)-1; % 绘制监测所得信号subplot(2,2,1);plot(t,x);title(原始信号);grid onset(gcf,color,w) set(gca,fontname,times New Roman)set(gca,fontsize,14.0)% 用db1小波对原始信号进行3层分解并提取小波系数c,l=wavedec(x,3,db1);%sym8ca3=appcoef(c,l,db1,3);%低频部分cd3=detcoef(c,l,3);%高频部分cd2=detcoef(c,l,2);%高频部分cd1=detcoef(c,l,1);%高频部分% 对信号进行强制去噪处理并图示cdd3=zeros(1,length(cd3);cdd2=zeros(1,length(cd2);cdd1=zeros(1,length(cd1);c1=ca3,cdd3,cdd2,cdd1;x1=waverec(c1,1,db1);subplot(2,2,2);plot(x1);title(强制去噪后信号);grid onset(gcf,color,w) set(gca,fontname,times New Roman)set(gca,fontsize,14.0)% 默认阈值对信号去噪并图示%用ddencmp( )函数获得信号的默认阈值，使用wdencmp( )函数实现去噪过程thr,sorh,keepapp=ddencmp(den,wv,x);x2=wdencmp(gbl,c,l,db1,3,thr,sorh,keepapp);subplot(2,2,3);plot(x2);title(默认阈值去噪后信号);grid onset(gcf,color,w) set(gca,fontname,times New Roman)set(gca,fontsize,14.0)% 给定的软阈值进行去噪处理并图示cd1soft=wthresh(cd1,x,1.465);%经验给出软阈值数cd2soft=wthresh(cd2,x,1.823); %经验给出软阈值数cd3soft=wthresh(cd3,x,2.768); %经验给出软阈值数c2=ca3,cd3soft,cd2soft,cd1soft;x3=waverec(c2,1,db1);subplot(2,2,4);plot(x3);title(给定软阈值去噪后信号);grid onset(gcf,color,w) set(gca,fontname,times New Roman)set(gca,fontsize,14.0)以上就是三中小波去噪的原程序。但红色标注的地方，我却运行不过去。提示分别问：1，? Error using = horzcatCAT arguments dimensions are not consistent.Error in = tt at 33c1=ca3,cdd3,cdd2,cdd1;2，? Error using = wthresh at 30Invalid argument value.Error in = tt at 54cd1soft=wthresh(cd1,x,1.465);%经验给出软阈值数请问为什么？我解决这个问题了，c1=a3 dd3 dd2 dd1c2=ca3 cd3soft cd2soft cd1soft;写一个小波去噪的matlab程序sound=wavread(000);sound=sound;subplot(4,1,1);plot(sound);title(原始信号);grid;set(gca,ytick,-1:1:1)count1=length(sound);noise=0.07*randn(1,count1);%count1=28000for i=1:count1signal(i)=sound(i)+noise(i);end;for i=1:count1y(i)=signal(i);end;subplot(4,1,2); plot(y);title(含噪信号);grid;set(gca,ytick,-1:1:1)%读出带噪语音信号，存为101.wavwavwrite(y,8000,16,c101);SNR1 = 10*log10(sum(sound.2)/sum(y-sound).2); %计算输入信噪比 title(Noise Added (SNR=,num2str(SNR1),dB); % num2str把数字转换成字符串C,L=wavedec(y,3,db3); %将信号y分解为3级ca=appcoef(C,L,db3,3); %提取一维近似系数，3表示级数，db3为字符串小波的名字cd1,cd2,cd3=detcoef(C,L,1,2,3); %3层小波分解one=mad(cd1); % mad计算样本数据平均绝对偏差two=mad(cd2); %30-38为阈值函数设置three=mad(cd3); n1=one/0.6745;n2=two/0.6745;n3=three/0.6745; men1=n1*sqrt(2*log(count1)/5; %固定阈值形式,阈值为与2*信号的长度，其中count1为信号长度men2=n2*sqrt(2*log(count1)/5;men3=n3*sqrt(2*log(count1)/5;E1=sum(abs(cd1).2)/length(cd1);E2=sum(abs(cd2).2)/length(cd2);E3=sum(abs(cd3).2)/length(cd3);E=1,E3/E1,E2/E1;Em=max(E);z=E3/E1;if (Em=1)&(E3/E10.8) f=1; cd1soft=wthresh(cd1,s,men1); %其中S表示软阈值，如果是H则表示硬阈值 cd2soft=wthresh(cd2,s,men2); cd3soft=wthresh(cd3,s,men3); c1=ca,cd3soft,cd2soft,cd1soft; else f=0; casoft=wthresh(ca,s,0.0050); cd1soft=wthresh(cd1,s,men1); cd2soft=wthresh(cd2,s,men2); cd3soft=wthresh(cd3,s,men3); c1=casoft,cd3soft,cd2soft,cd1soft; end; t=f; s3=waverec(c1,L,db3); s=s3; subplot(4,1,3);plot(s);title(消噪信号);grid;set(gca,ytick,-1:1:1)SNR2 = 10*log10(sum(sound.2)/sum(s-sound).2);wavwrite(s,8000,16,c102); 求小波去噪软阈值和硬阈值的matlab仿真程序硬阈值、软阈值这里有一段不知道有用没%设置信噪比和随机种子值snr=4;init=2055615866;%产生原始信号sref和高斯白噪声污染的信号ssref,s=wnoise(1,11,snr,init);%用db1小波对原始信号进行3层分解并提取系数c,l=wavedec(s,3,db1);a3=appcoef(c,l,db1,3);d3=detcoef(c,l,3);d2=detcoef(c,l,2);d1=detcoef(c,l,1);thr=1;%进行硬阈值处理ythard1=wthresh(d1,h,thr);ythard2=wthresh(d2,h,thr);ythard3=wthresh(d3,h,thr);c2=a3 ythard3 ythard2 ythard1;s3=waverec(c2,l,db1);%进行软阈值处理ytsoftd1=wthresh(d1,s,thr);ytsoftd2=wthresh(d2,s,thr);ytsoftd3=wthresh(d3,s,thr);c3=a3 ytsoftd3 ytsoftd2 ytsoftd1;s4=waverec(c3,l,db1);%对上述信号进行图示subplot(5,1,1);plot(sref);title(参考信号);subplot(5,1,2);plot(s);title(染噪信号);subplot(5,1,3);plot(s3);title(硬阈值处理);subplot(5,1,4);plot(s4);title(软阈值处理);求助Matlab小波语音去噪程序修改诸位我编了个小波语音去噪的程序，可是分帧清浊音判断后的效果不佳，不进行清浊音判断直接阈值效果反而好，能否修改一下，谢谢sound=wavread(c12345);count1=length(sound);noise=0.05*randn(1,count1);for i=1:count1y(i)=sound(i)+noise(i);endfigure(1)plot(sound);title(原始信号); set(gco,linewidth,1); set(gca,fontname,宋体,fontsize,10); xlabel(采样点,fontname,宋体,fontsize,10); ylabel(幅值,fontname,宋体,fontsize,10);figure(2)plot(y);title(含噪信号); set(gco,linewidth,1); set(gca,fontname,宋体,fontsize,10); xlabel(采样点,fontname,宋体,fontsize,10); ylabel(幅值,fontname,宋体,fontsize,10);%在小波基db3下进行一维离散小波变换N=250; n=fix(count1/N);t=; s = 0 0;for m=1:n xd=y(N*(m-1)+1:N+N*(m-1); C,L=wavedec(xd,4,sym4); ca=appcoef(C,L,sym4); cd1,cd2,cd3,cd4=detcoef(C,L,1,2,3,4); one=median(abs(cd1); two=median(abs(cd2); three=median(abs(cd3); four=median(abs(cd4); five=median(abs(ca); n1=one/0.6745; n2=two/0.6745; n3=three/0.6745; n4=four/0.6745; n5=five/0.6745; men1=n1*sqrt(2*log(count1)/log(1+1); men2=n2*sqrt(2*log(count1)/log(2+1); men3=n3*sqrt(2*log(count1)/log(3+1); men4=n4*sqrt(2*log(count1)/log(4+1); men5=n5*sqrt(2*log(count1)/log(4+1); E1=sum(abs(cd1).2)/length(cd1); E2=sum(abs(cd2).2)/length(cd2); E3=sum(abs(cd3).2)/length(cd3); E4=sum(abs(cd4).2)/length(cd4); E=1,E4/E1,E3/E1,E2/E1; Em=max(E); z=E4/E1;if (Em=1)&(E4/E10.9) f=1; cd1soft=wthresh(cd1,h,men1); cd2soft=wthresh(cd2,h,men2); cd3soft=wthresh(cd3,h,men3); cd4soft=wthresh(cd4,h,men4); c1=ca,cd4soft,cd3soft,cd2soft,cd1soft; else f=0; casoft=wthresh(ca,h,men5); cd1soft=wthresh(cd1,h,men1); cd2soft=wthresh(cd2,h,men2); cd3soft=wthresh(cd3,h,men3); cd4soft=wthresh(cd4,h,men4); c1=casoft,cd4soft,cd3soft,cd2soft,cd1soft; end; t=f s3=waverec(c1,L,sym4); s=s s3; figure(3) plot(s); title(去噪后信号); set(gco,linewidth,1); set(gca,fontname,宋体,fontsize,10); xlabel(采样点,fontname,宋体,fontsize,10); ylabel(幅值,fontname,宋体,fontsize,10); wavwrite(y,8000,16,c101); wavwrite(s,8000,16,c102); end;loadleleccum%将信号装入MATLAB的工作环境s=leleccumsubplot(221);plot(s);%画出原始信号波形title(原始信号);%下面利用默认阈值进行消噪处理thr,sorh,keepapp=ddencmp(den,wv,s);%获得信号的默认阈值s2=wdencmp(gbl,c,l,db1,3,thr,sorh,keepapp);subplot(223);plot(s2);title(默认阈值消噪后的信号);grid;%下面利用给定的软阈值进行消噪cd1soft=wthresh(cd1,s,t);cd2soft=wthresh(cd2,s,t);cd3soft=wthresh(cd3,s,t);c2=ca3,cd3soft,cd2soft,cd1soft;s3=waverec(c2,l,db1);%C2，L为新的分解结构subplot(224);plot(s3);title(给定软阈值消噪后的信号);grid硬阈值：Y=wthresh(X,h,T)不知对否，大家给意见给你一个小波应用软阈值和硬阈值处理，然后计算信噪比snr和均方差的mse的程序！你可以参考一下！%-小波分析心电信号-E=M(:,2);E=E;n=size(E);s=E(1:2000);%使用db小波对s进行3层分解for jj=1:8 %jj表示小波的类型 switch jj case 1 db=db1; case 2 db=db2; case 3 db=db3; case 4 db=db4; case 5 db=db5; case 6 db=db6; case 7 db=db7; case 8 db=db8; otherwise end;C L=wavedec(E,3,db);% 从c中提取尺度3下的近似小波系数cA3=detcoef(C,L,3);%从信号c中提取尺度3，2，1下的细节小波系数cD1,cD2,cD3,cD4,cD5=detcoef(C,L,1,2,3,4,5);%重构尺度5下的近似小波系数A5=wrcoef(a,C,L,db,5);% 重构尺度3,2,1下的细节小波系数. D1=wrcoef(d,C,L,db,1);D2=wrcoef(d,C,L,db,2);D3=wrcoef(d,C,L,db,3);D4=wrcoef(d,C,L,db,4);D5=wrcoef(d,C,L,db,5);%-使用阈值法去噪-%利用ddencmp得到除噪的默认参数%thr,sorh,keepapp分别表示是阈值,软阈值(或硬阈值)和允许用户保存的低频系数.%den,wv,s分别为降噪(cmp表示压缩),小波(wp小波包),和降噪信号.thr,sorh,keepapp=ddencmp(den,wv,E);%去噪clean=wdencmp(gbl,C,L,db,5,thr,sorh,keepapp);%-去噪效果衡量（SNR越大效果越好,MSE越小越好）-%选取信号的长度。N=n(2);x=E;y=clean;F=0;M=0;for ii=1:N m(ii)=(x(ii)-y(ii)*(x(ii)-y(ii); t(ii)=y(ii)*y(ii); f(ii)=t(ii)/m(ii); F=F+f(ii); M=M+m(ii);end;SNR(jj)=10*log10(F);MSE(jj)=M/N;end;%对比原始信号和除噪后的信号subplot(2,1,1);plot(