jiance.rar_matlab语音合成_共振峰 合成_语音_语音合成

function [fmap,pt2] = ftrack(y,fs) % FTRACK Tracks the formants through a method of averaging different resolutions % of time frequency lpc based spectrograms. In addition it also determines the % pitch contour of the speech signal. % % This is a very preliminary attempt at tracking. IT SHOULD THEREFORE BE USED % WITH DUE CAUTION. % % Satrajit Ghosh, 2002 % SpeechLab, Department of Cognitive and Neural Systems, Boston University bDisplay = 1; Fsamps = 256; % sampling resolution in frequency dimension Tsamps = round(length(y)/18000*256); % sampling resolution in time dimension % The final maps are generated such that time is divided into Tsamps % number of points. This should be scaled with length of speech input % Frequency dimension is broken into Fsamps number of points. tmp_img1 = zeros(Fsamps,Tsamps); ct = 0; numiter = 8; % number of iterations to run. seemed like a good number % Iterate through different frequency resolutions. The time resolution is % automatically determined as time_pts*freq_pts = length of signal for i=2.^(8-8*exp(-linspace(1.5,10,numiter)/1.4)), % Determine the time-frequency distribution at the current [tmp_img1,ft1,pt1] = lpcsgram(y,fix(length(y)/round(i)),round(i),fs); % Get the dimensions of the output time-frequency image [M,N] = size(tmp_img1); % Create a grid of the final resolution [xi,yi] = meshgrid(linspace(1,N,Tsamps),linspace(1,M,Fsamps)); % Interpolate returned TF image to final resolution tmp_img2 = interp2(tmp_img1,xi,yi); ct = ct+1; % Interpolate formant tracks and pitch tracks pt2(:,ct) = interp1([1:length(pt1)]',pt1(:),linspace(1,length(pt1),Tsamps)'); ft2(:,:,ct) = interp1(linspace(1,length(y),fix(length(y)/round(i)))',Fsamps*ft1',linspace(1,length(y),Tsamps)')'; % Normalize tmp_img3(:,:,ct) = tmp_img2/max(tmp_img2(:)); if bDisplay, subplot(221);imagesc(tmp_img1);axis xy; subplot(222);imagesc(tmp_img2);axis xy; subplot(223);imagesc(squeeze(mean(tmp_img3,3)));axis xy; drawnow; end; end % Determine mean tfr image and formant track tmp_img4 = squeeze(mean(tmp_img3,3)); % tfr ft3 = squeeze(nanmean(permute(ft2,[3 2 1]))); % if bDisplay, subplot(224);imagesc(tmp_img4);axis xy; hold on; plot(ft3,'y'); end; % convert fmnts to image tmap = repmat([1:Tsamps]',1,3); idx = find(~isnan(sum(ft3,2))); fmap = ft3(idx,:); tmap = tmap(idx,:); % filter formant tracks to remove noise [b,a] = butter(9,0.1); fmap = round(filtfilt(b,a,fmap)); pt3 = nanmean(pt2'); pt3 = (pt3-nanmin(pt3))/(nanmax(pt3)-nanmin(pt3)); % Rescaling is done after display code if bDisplay, imap = zeros(Fsamps,Tsamps); ind = sub2ind(size(imap),fmap(:),tmap(:)); imap(ind) = 1; tpts = tmap(:,1); figure; subplot(221); imagesc(imap);axis xy;hold on; plot(tpts,fmap(:,1),tpts,fmap(:,2),tpts,fmap(:,3)); idx = [1:5]'; plot(tpts(idx),fmap(idx,1),'.-',tpts(idx),fmap(idx,2),'.-',tpts(idx),fmap(idx,3),'.-'); subplot(222); % Create a wider formant track anisomask = anisodiff(imap,6,50,0.01,1); imagesc(anisomask>0);axis xy;hold on; plot(tpts,fmap(:,1),tpts,fmap(:,2),tpts,fmap(:,3)); idx = [1:5]'; plot(tpts(idx),fmap(idx,1),'.-',tpts(idx),fmap(idx,2),'.-',tpts(idx),fmap(idx,3),'.-'); subplot(223); imagesc(tmp_img4);axis xy;hold on; plot(tpts,fmap(:,1),'r',tpts,fmap(:,2),'r',tpts,fmap(:,3),'r'); idx = [1:5]'; plot(tpts(idx),fmap(idx,1),'.-',tpts(idx),fmap(idx,2),'.-',tpts(idx),fmap(idx,3),'.-'); subplot(224); imagesc(tmp_img4.*(anisomask>0));axis xy;hold on; plot(tpts,fmap(:,1),'r-',tpts,fmap(:,2),'r-',tpts,fmap(:,3),'r-'); % idx = [1:5]'; % plot(tpts(idx),fmap(idx,1),'.-',tpts(idx),fmap(idx,2),'.-',tpts(idx),fmap(idx,3),'.-'); plot(256*pt3,'y.-'); end; % Rescale to Actual Formants and take the mean of pitch tracks fmap = (fs/2)*(fmap/256); pt2 = nanmean(pt2');