卡尔曼声音增强课程设计 代码加报告 含音频

function enhancedsignal=kalmanwhite(noisyspeech,samplefrequency) %% preprocess x=noisyspeech'; fs=samplefrequency; nx=length(x); enhanced_x=zeros(1,nx); %% separate frame FrameLen=fix(0.025*fs); overlap=0; inc=FrameLen-overlap; % frame offset x_frame=[]; temp=x; while true if length(temp) <= FrameLen x_frame=[x_frame; temp(1:end) zeros(1,FrameLen-length(temp))]; break; else x_frame=[x_frame; temp(1:FrameLen)]; end temp=temp(inc+1:end); end frame_num=size(x_frame,1); p_order=8; %% noise estimate NNoise=20; noise=x(1:NNoise*FrameLen); VN=var(noise); %% kalman filter for each frame for i=1:frame_num if i==1 [Metric,VS]=lpc(x_frame(i,:),p_order); else [Metric,VS]=lpc(xn(p_order,:),p_order); end if (VS-VN>0) VS=VS-VN; else VS=0.0005; end %% state matrix F=[zeros(p_order-1,1) eye(p_order-1); -1*fliplr(Metric(2:p_order+1))]; % status transfer matrix H=[zeros(1,p_order-1) 1]; % observation matrix G=transpose(H); % input matrix xn=zeros(p_order,FrameLen); %% set init state and error variance if i==1 x0=zeros(p_order,1); P0=zeros(p_order,p_order); else x0=xn(p_order,(FrameLen-p_order+1:FrameLen))'; P0=Pn; end %% fliter for k=1:FrameLen % predict status if(k==1) xn(:,k)=F*x0; else xn(:,k)=F*xn(:,k-1); end % predict covariance matrix Pm=F*P0*F'+G*VS*G'; % kalman gain K=Pm*H'*inv(H*Pm*H'+VN); % update xn(:,k)=xn(:,k)+K*(x_frame(i,k)-H*xn(:,k)); Pn=(eye(p_order)-K*H)*Pm; end %% get enhanced x_i enhanced_x(1+(i-1)*inc:(i-1)*inc+FrameLen)=xn(p_order,:); end enhancedsignal=enhanced_x';