FILTER_BANK-16.zip_filter bank

% Example_12_7 % % Created for the book: % Ljiljana D. Milic, % Multirate Filtering for Digital Signal Processing: MATLAB Applications % Information Science Reference, Hershey, New York, 2009 % % Analysis and synthesis octave bank clear all close all N = 18; fp = 0.4; % Seting the input parameters for firpr2chfb [h0,h1,g0,g1] = firpr2chfb(N-1,fp); % Generating four filters for the orthogonal FIR filter bank [H0,f] = freqz(h0,1,512,2); [H1,f] = freqz(h1,1,512,2); figure (1) %plot(f,(abs(H0)),f,(abs(H1)),'b'), axis([0,1,0,1.1]) %hold on %g(1)=axes('Position',[0.17 0.75 0.2 0.1]); %g(1)=axes('Position',[0.24 0.70 0.25 0.1]); %plot(f(1:205),(abs(H0(1:205)))), %axis([0,0.4,0.9998,1.00002]) %hold on %g(2)=axes('Position',[0.62 0.70 0.25 0.1]); %plot(f(307:512),(abs(H1(307:512)))), %axis([0.6,1,0.9998,1.00002]) %axis([0 1.1*fp/2 -2*Rp 2*Rp]), grid %hold on wname = 'db9'; % Set wavelet name. % Compute the four filters associated with wavelet name given by the input string wname. [h0,h1,g0,g1] = wfilters(wname); N = 18; % The length of db9 filter [H00,f]=freqz(h0,1,512,2); [H11,f]=freqz(h1,1,512,2); figure (1) plot(f,(abs(H0)),'--',f,(abs(H1)),'--b',f,(abs(H00))/sqrt(2),'b',f,(abs(H11))/sqrt(2),'b'), grid xlabel('Normalized frequency \omega/\pi'), ylabel('Magnitude') axis([0,1,0,1.1]) hold on g(1)=axes('Position',[0.20 0.65 0.25 0.1]); plot(f(1:209),(abs(H00(1:209)))/sqrt(2),f(1:209),(abs(H0(1:209))),'--b'), axis([0,0.41,0.99,1.005]) g(2)=axes('Position',[0.62 0.65 0.25 0.1]); plot(f(305:512),(abs(H11(305:512)))/sqrt(2),f(305:512),(abs(H1(305:512))),'--b'), axis([0.59,1,0.99,1.005]) %axis([0 1.1*fp/2 -2*Rp 2*Rp]), grid % Compute the sum of the square magnitude responses ver=abs(H0).^2+abs(H1).^2; % Test signal x=[zeros(size(1:20)),ones(size(21:50)),zeros(size(51:511))]; %x=[zeros(size(1:100)),0.01*(1:150),zeros(size(251:511))]; figure (2) subplot(3,1,1) plot(x) title('Test signal') xlabel('Time index n') axis([0,512,-0.2,1.2]) % Analysis part % Level 1 x0 = filter(h0,1,x); x1 = filter(h1,1,x); v0 = downsample(x0,2); v1 = downsample(x1,2); % Level 2 x2 = v0; x02 = filter(h0,1,x2); x12 = filter(h1,1,x2); v02 = downsample(x02,2); v12 = downsample(x12,2); v2 = v12; % Level 3 x3 = v02; x03 = filter(h0,1,x3); x13 = filter(h1,1,x3); v03 = downsample(x03,2); v13 = downsample(x13,2); v3 = v13; % Level 4 x4 = v03; x04 = filter(h0,1,x4); x14 = filter(h1,1,x4); v04 = downsample(x04,2); v14 = downsample(x14,2); v4 = v14; v5 = v04; figure (3) subplot(5,1,1) plot(v1) ylabel('v_1[n]') axis([0,256,-0.5,0.5]) subplot(5,1,2) plot(v2) ylabel('v_2[n]') axis([0,256,-0.5,0.5]) subplot(5,1,3) plot(v3) ylabel('v_3[n]') axis([0,256,-0.5,1]) subplot(5,1,4) plot(v4) ylabel('v_4[n]') axis([0,256,-0.5,0.5]) subplot(5,1,5) plot(v5) ylabel('v_5[n]') xlabel('Time index n') axis([0,256,-0.5,4]) % Synthesis part % Level 4 w04=upsample(v5,2); w14=upsample(v4,2); y3=filter(g0,1,w04) + filter(g1,1,w14); % Level 3 w13 = [zeros(size(1:N-1)),v3(1:length(v3)-(N-1))]; w13 = upsample(w13,2); yu3 = upsample(y3,2); y2 = filter(g0,1,yu3) + filter(g1,1,w13); % Level 2 w12 = [zeros(size(1:3*(N-1))),v2(1:length(v2)-3*(N-1))]; w12 = upsample(w12,2); yu2 = upsample(y2,2); y1 = filter(g0,1,yu2) + filter(g1,1,w12); % Level 1 w11 = [zeros(size(1:7*(N-1))),v1(1:length(v1)-7*(N-1))]; w11 = upsample(w11,2); yu1 = upsample(y1,2); y = filter(g0,1,yu1) + filter(g1,1,w11); % Reconstructed signal figure (4) %subplot(5,1,1) %plot(v04) %ylabel('v_5[n]') %title ('Banka sinteze: rekonstrukcija signala') %axis([0,64,-0.2,1.2]) subplot(4,1,1) plot(y3) ylabel('y_3(n)') axis([0,512,-0.4,3.4]) subplot(4,1,2) plot(y2) ylabel('y_2[n]') axis([0,512,-0.4,2.4]) subplot(4,1,3) plot(y1) axis([0,512,-0.2,1.2]) ylabel('y_1[n]') axis([0,512,-0.4,2.4]) subplot(4,1,4) plot(y) ylabel('y[n]') xlabel('Time index n') axis([0,512,-0.2,1.2]) length(y)