📄 zm01xg_5_ed2.m
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clear all;
% [y1,fs,bits]=wavread('F:\noise enhancing\wav\今天雨好大.wav'); %input clean speech今天雨好大
% y1=y1/max(abs(y1));%语音信号归一化
% %wavwrite(y1,8000,8,'F:\noise enhancing\voise\5.wav');
% figure(1);
% plot(y1); %depict clean speech
%
% % [noise,fs1,bits1]=wavread('F:\noise enhancing\wav\今天雨好大_noise.wav'); %input noise今天雨好大_noise
% % y=mixsig(y1,noise,0); % mix clean speech with noise according to definite SNR
% % y=y/max(abs(y));%归一化
% % wavwrite(y,8000,8,'F:\noise enhancing\wav\mymasking_s&w(5).wav');
% % figure(2);
% % plot(y); %depict noisy speech
%
% [noise,fs1,bits1]=wavread('F:\noise enhancing\wav\今天雨好大_noise.wav');
% y=mixsig(y1,noise,0);% 混合
% y=y/max(abs(y));%归一化
% wavwrite(y,8000,8,'F:\noise enhancing\wav\mymasking_s&w(0).wav');
% figure(2);
% plot(y);
[y,fs,bits]=wavread('F:\noise enhancing\noise\denoise\stc-launch[1].wav');
%[y,fs,bits]=wavread('F:\noise enhancing\wav\5.wav');%mys&w16bit.F:\noise enhancing\wav\wav zhuiqiu_s&w(10) mys&w16bit
frame = 256; % Defining frame size
shift=128;
win=hamming(256);
for j1 = 1:length(y),
signal(j1) = y(j1);
end;
ps_noise=zeros(length(signal)/frame,frame); %initialize
frame_temp = zeros(length(signal)/frame,frame);
hh = 0;
for k = 1 : 5,
for l = 1 : frame,
b(l) = signal(hh+l);
end;
hh = hh + frame;
frame_temp(k,1:frame) = abs(fft(b)); %fft for the first 5 frames
ps_noise(k,1:frame) = (frame_temp(k,1:frame).*conj(frame_temp(k,1:frame)))/frame; %power
%ps_noise(1,1:frame)= (sum(ps_noise(1:k,l))/20); % Sum of the power spectral densities of samples within a frame
end;
ps_noise(1,1:frame)= (sum(ps_noise(1:k,1:frame))/5); % average power spectral densities of noises within a frame
head = 0;
%%%%% START OF THE NOISE ELIMINATION THROUGH SPECTRAL SUBTRACTION BASED ON THE THRESHOLD SET %%%%%%
for k = 1 :( length(signal)/shift-1),
for m = 1 : frame,
abc1(m) = signal(head+m);
end;
head = head +shift;
frame_temp(k,1:frame) = abs(fft(abc1)); % FFT OF THE SIGNAL + NOISE FRAME BY FRAME
frame_angle(k,1:frame) = angle(fft(abc1)); % ANGLE OF FFT OF THE SIGNAL + NOISE FRAME BY FRAME
pmix_signal(k,1:frame) = (frame_temp(k,1:frame).*conj(frame_temp(k,1:frame)))./frame; % power of mixed signal
ps_temp(k,1:frame)=pmix_signal(k,1:frame);
%%%%%%%%%%% end detection %%%%%%%%%%%%%%%
c_n=0.995; %%% rate of renewing noise power spectrum
c_s=0.999; %%% rate of renewing signal power spectrum
c_beta=0.1;
counter=0; %%% the number of consecutive noise frames till current frame
if k==1 % first frame
ps_signal(k,1:frame)=pmix_signal(k,1:frame);
counter=1;
else
yeta(1,k)=sum( pmix_signal(k,1:frame)/(abs(pmix_signal(k,1:frame)-ps_noise(k-1,1:frame))) );
if(yeta(1,k)>1.1) %if yeta is larger than threshold,current frame is signed as noise frame
counter=counter+1;
if counter==3 %if three consecutive frames are signed as noise frame,renew the estimation of noise energy
ps_noise(k,1:frame)=c_n*ps_noise(1,1:frame)+(1-c_n)*pmix_signal(k,1:frame);
counter=2;
else ps_noise(k,1:frame)=ps_noise(k-1,1:frame);
end
else
ps_noise(k,1:frame)=ps_noise(k-1,1:frame);
counter=0;
end;
ps_signal(k,1:frame)=c_s*ps_signal(k-1,1:frame)+(1-c_s)*pmix_signal(k,1:frame)-c_beta*ps_noise(k,1:frame); %%% estimation of power spectrum of signal
end
ps_signal(k,1:frame)=abs(ps_signal(k,1:frame));
%%%%%%%%% calculate masking value %%%%%%%%%
T=zeros(1,256);
b=zeros(1,22);c=zeros(1,22);o=zeros(1,22);
sf=zeros(22,22);
%%% calculate power spectrum of every bark band
for i1=1:3,
b(1)=b(1)+ps_signal(k,i1);
end;
for i1=4:6,
b(2)=b(2)+ps_signal(k,i1);
end;
for i1=7:10,
b(3)=b(3)+ps_signal(k,i1);
end;
for i1=11:13,
b(4)=b(4)+ps_signal(k,i1);
end;
for i1=14:16,
b(5)=b(5)+ps_signal(k,i1);
end;
for i1=17:20,
b(6)=b(6)+ps_signal(k,i1);
end;
for i1=21:25,
b(7)=b(7)+ps_signal(k,i1);
end;
for i1=26:29,
b(8)=b(8)+ps_signal(k,i1);
end;
for i1=30:35,
b(9)=b(9)+ps_signal(k,i1);
end;
for i1=36:41,
b(10)=b(10)+ps_signal(k,i1);
end;
for i1=42:47,
b(11)=b(11)+ps_signal(k,i1);
end;
for i1=48:55,
b(12)=b(12)+ps_signal(k,i1);
end;
for i1=56:64,
b(13)=b(13)+ps_signal(k,i1);
end;
for i1=65:74,
b(14)=b(14)+ps_signal(k,i1);
end;
for i1=75:86,
b(15)=b(15)+ps_signal(k,i1);
end;
for i1=87:101,
b(16)=b(16)+ps_signal(k,i1);
end;
for i1=102:118,
b(17)=b(17)+ps_signal(k,i1);
end;
for i1=119:141,
b(18)=b(18)+ps_signal(k,i1);
end;
for i1=142:170,
b(19)=b(19)+ps_signal(k,i1);
end;
for i1=171:205,
b(20)=b(20)+ps_signal(k,i1);
end;
for i1=206:246,
b(21)=b(21)+ps_signal(k,i1);
end;
for i1=247:256,
b(22)=b(22)+ps_signal(k,i1);
end;
%%% calculate the spread function
for i1=1:22,
sf(i1)=15.81+7.5*(i1+0.474)-17.5*sqrt(1+(i1+0.474)*(i1+0.474));
end;
%%% apply the spread function to the critical band spectrum
% for j1=1:22,
% c(j1)=0;
% for i1=1:22,
% c(j1)=c(j1)+b(i1)*sf(i1,j1);
% end;
% end; % the function of this section is same as the following function(conv2)
cc_temp=conv2(b,sf); % convolution
for i1=1:22
for j1=1:22
c(i1)=c(i1)+cc_temp(i1,j1);
end;
end;
%%% calculate eh spread masking threshold
temp_value(k)=0.0;
for i1=1:22,
temp_value(k)=temp_value(k)+b(i1);
end;
ua=temp_value(k)/256.0; % calculate the arithmetic average
temp_value(k)=0;
for i1=1:256,
temp_value(k)=temp_value(k)+log10(ps_signal(k,i1));
end;
temp_value(k)=temp_value(k)/256.0;
uj=power(10,temp_value(k)); % calculate the geomitry average
sfm=-10*log10(uj/ua); % spectrual flatness measurement
u=min(sfm/(-60),1); % tonality of signal
for i1=1:22,
O(i1)=u*(14.5+i1)+(1-u)*5.5; % offset
T(i1)=power(10,log10(c(i1))-O(i1)/10); % auditory masking threshold
c(i1)=T(i1);%c[i]暂存T[i]
end;
for i1=1:3,
T(i1)=c(1);
end;
for i1=4:6,
T(i1)=c(2);
end;
for i1=7:10,
T(i1)=c(3);
end;
for i1=11:13,
T(i1)=c(4);
end;
for i1=14:16,
T(i1)=c(5);
end;
for i1=17:20,
T(i1)=c(6);
end;
for i1=21:25,
T(i1)=c(7);
end;
for i1=26:29,
T(i1)=c(8);
end;
for i1=30:35,
T(i1)=c(9);
end;
for i1=36:41,
T(i1)=c(10);
end;
for i1=42:47,
T(i1)=c(11);
end;
for i1=48:55,
T(i1)=c(12);
end;
for i1=56:64,
T(i1)=c(13);
end;
for i1=65:74,
T(i1)=c(14);
end;
for i1=75:86,
T(i1)=c(15);
end;
for i1=87:101,
T(i1)=c(16);
end;
for i1=102:118,
T(i1)=c(17);
end;
for i1=119:141,
T(i1)=c(18);
end;
for i1=142:170,
T(i1)=c(19);
end;
for i1=171:205,
T(i1)=c(20);
end;
for i1=206:246
T(i1)=c(21);
end;
for i1=247:256,
T(i1)=c(22);
end;
%////计算绝对听阈////
mm=0.0;
for i1=1:256,
f(i1)=mm;
mm=mm+8/256;
end;
f(1)=f(2);
for i1=1:256,
%f(i1)=(3.64*power(f(i1),-0.8)-6.5*exp(-0.6*power((f(i1)-3.3),2))+0.001*power(f(i1),4));
f(i1)=3.64*(f(i1).^(-0.8))- 6.5*exp(-0.6*((f(i1)-3.3).^2))+0.001*(f(i1).^4);
end;
for i1=1:256,
T(i1)=max(T(i1),f(i1));
end;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%% original algorithm %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% tmax=0;
% tmin=0;
% for i1=1:256,
% if(T(i1)>tmax)
% tmax=T(i1);
% end;
% if(T(i1)<tmin)
% tmin=T(i1);
% end;
% end;
%frame_ps(1,k) = (sum(ps_signal(k,1:frame)));% Sum of the power spectral densities of samples within a frame
%ps_final(1,k) = frame_ps(1,k) - threshold;% Elimination of noise from the corrupted signal
%h(1,k)=abs(ps_final(1,k)/frame_ps(1,k));
%aa=0.6;bb=2;
% for k1=1:256,
% alfa(k1)=(tmax*6-T(k1)*6+1*T(k1)-1*tmin)/(tmax-tmin);
% beita(k1)=(tmax*0.02-T(k1)*0.02+0*T(k1)-0*tmin)/(tmax-tmin);
% temppow=abs(ps_noise(k,k1))/abs(ps_temp(k,k1));
%
% if(power(temppow,2)<(1.0/(alfa(k1)+beita(k1))))
% %if(1-alfa[k]*pow(temppow,2)>0)
% frame1( k,k1)=power((1-alfa(k1)*power(temppow,2)),0.5)*ps_temp(k,k1);
% else
% frame1(k,k1)=power((beita(k1)*power(temppow,2)),0.5)*ps_temp(k,k1);
% end;
% end;
% end;
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%% new algorithm %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% a1(k,1:frame)=power((ps_signal(k,1:frame)+ps_noise(k,1:frame)),2)./(power((sqrt(ps_signal(k,1:frame))+sqrt(T(1:frame))),2))-(ps_signal(k,1:frame)+ps_noise(k,1:frame));
% a2(k,1:frame)=power((ps_signal(k,1:frame)+ps_noise(k,1:frame)),2)./(power((sqrt(ps_signal(k,1:frame))-sqrt(T(1:frame))),2))-(ps_signal(k,1:frame)+ps_noise(k,1:frame));
a1(k,1:frame)=power((ps_signal(k,1:frame)+ps_noise(k,1:frame)),2)./(ps_signal(k,1:frame))-(ps_signal(k,1:frame)+ps_noise(k,1:frame));
a2(k,1:frame)=power((ps_signal(k,1:frame)+ps_noise(k,1:frame)),2)./(T(1:frame))-(ps_signal(k,1:frame)+ps_noise(k,1:frame));
a2=abs(a2);
% if a1(k,1:frame)>a2(k,1:frame)
% a(k,1:frame)=a1(k,1:frame);
% else a(k,1:frame)=a2(k,1:frame);
% end
landa=1.5; %%% make up for estimate error of a_l & a_h
c_a=0.7; %%% coefficient of a_l & a_h
a(k,1:frame)=(c_a*a1(k,1:frame)+(1-c_a)*a2(k,1:frame))*landa;
%a(k,1:frame)=(ps_noise(k,1:frame)+T(1:frame)).*(ps_noise(k,1:frame)/T(1:frame));
e_ps_signal(k,1:frame)=power(pmix_signal(k,1:frame),2)./(pmix_signal(k,1:frame)+a(k,1:frame));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Weiner Filter %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% hh = 0;
% for k = 1 : 5,
% ps_noise(k,1:frame) = (frame1(k,1:frame).*conj(frame1(k,1:frame)))/frame;
% end;
% %语音前五真早声能量
% ps_noise2(1,1:frame)= (sum(ps_noise(1:5,1:frame))/5);
% % frame1_initial(1:frame)=sum(frame1(1:5,1:frame))/5;
%
% for k = 1 : (length(signal)/shift-3),
% ps_noise(k,1:frame)=ps_noise2(1,1:frame);
% ps_signal(k,1:frame) = (frame1(k,1:frame).*conj(frame1(k,1:frame)))./frame;
% frame_ps(1,k) = (sum(ps_signal(k,1:frame)));%一阵内信号能量之和
% frame_pn(1,k)=sum(ps_noise(k,1:frame));
% ps_final(1,k) = frame_ps(1,k)-0* frame_pn(1,k);
%
% %ps_final(k,1:frame) = ps_signal(k,1:frame)-ps_noise(k,1:frame);
% %if ps_final(k,1:frame)<0
% % ps_final(k,1:frame)=zeros(1,frame);
% %end
%
% %frame1(k,1:frame)=sqrt(ps_final(k,1:frame));
%
% if ps_final(1,k)<0
% ps_final(1,k)=0;
% end
%
% aa=1;bb=0.5;
% h(1,k)=power(ps_final(1,k)/(ps_final(1,k)+aa*frame_pn(1,k)),bb);
% frame1(k,1:frame) = h(1,k).*(frame1(k,1:frame));%为那滤波
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
frame1(k,1:frame) = sqrt(e_ps_signal(k,1:frame)).*(exp(i*frame_angle(k,1:frame)));
frame2(k,1:frame)=ifft(frame1(k,1:frame));
if k==1
signal(1,1:shift)=frame2(k,1:shift);
% else if k==2
else signal(1,((k-1)*shift+1):(k*shift))=(frame2(k,1:shift)+frame2(k-1,(shift+1):(shift*2)))/2;
% else if k==3
% signal(1,(shift*2+1):(3*shift))=(frame2(k,1:shift)+frame2(k-1,(shift+1):(shift*2))+frame2(k-2,(shift*2+1):(shift*3)))/3;
% %else if k==length(signal)/shift-3
% %signal(1,(k*shift+1):((k+1)*shift))=frame2(k,(3*shift+1):frame);
% else signal(1,(((k-1)*shift+1):(k*shift)))=(frame2(k,1:shift)+frame2(k-1,(shift+1):(shift*2))+frame2(k-2,(shift*2+1):(shift*3))+frame2(k-3,(shift*3+1):frame))/4;
% %end;
% end;
% end;
end;
%signal(1,(((k-1)*frame)+1):(k*frame)) = frame2(k,1:frame); % Retriving back the signal(after spectral subtraction)
end
signal((length(y)-1000):(length(y)))=[]; %give up 4 frames in the end
signal(1:1280)=[];
% y1((length(y1)-1000):(length(y1)))=[];
% y1(1:1280)=[];
%figure(3);
%plot(1:length(frame_ps),frame_ps,1:length(ps_signal),ps_signal);
figure(4);
signal=signal';
signal=signal/max(abs(signal));%归一化
plot(1:length(signal),signal);
%overall_snr = 10*log10(sum(abs(y1).^2)/sum((abs(y1-signal)).^2))
%overall_snr2 = 10*log10(sum(abs(y1).^2)/sum((abs(y1-signal*1.2)).^2))
wavwrite(signal,11025,16,'F:\noise enhancing\noise\denoise\stc-launch[1]_denoise.wav');
% for i=33:length(signal)
% signal3(i)=signal(i)+signal(i-32);
% end
%
% wavwrite(signal3,8000,8,'F:\noise enhancing\wav\16bitmymasking21(5db).wav');
% for i=33:length(signal)
% signal4(i)=signal(i)+signal(i-16);
% end
% wavwrite(signal4,8000,8,'F:\noise enhancing\wav\mymasking21(-5db).wav');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 以下画语谱图 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%map=(log10(1:0.1:10))';
%map=[map map map ];
%subplot(2,2,1)
%spgrambw( noise,8000);title('noise Specgram');colormap(1-map);
%subplot(2,2,2)
%spgrambw(y1,8000);title('sig Specgram');colormap(1-map);
%subplot(2,2,3)
%spgrambw(y,8000);title('mixsig Specgram');colormap(1-map);
%subplot(2,2,4)
%spgrambw(signal,8000);title('Proposed Algorithm Specgram');colormap(1-map);
%spgrambw(sig,8000);title('pure Specgram');colormap(1-map);
%[overall_snr2,seg_nr2]=snr(y1,signal)
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