diff toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/WhiteVowel.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/WhiteVowel.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,80 @@
+function [output,aCoeff] = WhiteVowel(data,sr,L,pos)
+% function [output,aCoeff] = WhiteVowel(data,sr,L,pos)
+%
+% Speech is often described as having spectral peaks or formants which 
+% identify the phonetic signal. An interesting experiment, first proposed by
+% XXX, filters a speech signal to remove all the formant information at one
+% time during the speech. If there are no formant peaks, how can the speech
+% be understood?  It turns out that processing, much like RASTA, means that
+% relative changes in spectrum are the most important, thus the speech signal
+% is understood because the formant transitions carry the information.  This
+% gives speech an important transparency due 
+%
+% This function takes a speech signal (data) with a given sampling rate (sr).
+% It then finds the L-order LPC filter that describes the speech at the given
+% position (pos ms).  The entire speech signal is then filtered with the
+% inverse of the LPC filter, effectively turning the speech spectrum at the 
+% given time white (flat).
+
+% Chris Pal, Interval, May 1997
+% (c) 1998 Interval Research Corporation  
+
+fr = 20; fs = 30; preemp = .9378;			% LPC defaults
+
+[row col] = size(data);
+if col==1 data=data'; end
+
+nframe = 0;
+msfr = round(sr/1000*fr);
+msfs = round(sr/1000*fs);
+duration = length(data);
+msoverlap = msfs - msfr;
+frameNumber = floor(pos/1000*sr/msfr);
+
+frameStart = round(pos/1000*sr - msfs/2);	                
+frameData = data(frameStart:(frameStart+msfs-1));   
+aCoeff = proclpc(frameData, sr, L, fr, fs, preemp);
+                                % Calculate the filter response
+                                % by evaluating the z-transform
+spec=lpc_spec(aCoeff);
+subplot(2,3,1);
+plot(spec);
+title('LPC Spectral Slice');
+ylabel('Original')
+
+								% Now do the actual whitening filter
+output = filter(aCoeff,1,data)';
+
+frameData = output(frameStart:(frameStart+msfs-1));   
+bCoeff = proclpc(frameData, sr, L, fr, fs,  preemp);
+spec=lpc_spec(bCoeff);
+subplot(2,3,4);
+plot(spec);
+ylabel('Whitened'); xlabel('FFT Bin');
+
+% 256-DFT
+origSpec = 20*log10(abs(specgram(data,512,sr,msfs,msoverlap)));
+subplot(2,3,2),imagesc(origSpec); axis xy; colormap(1-gray);
+title('Spectrogram');
+
+synSpec = 20*log10(abs(specgram(output,512,sr,msfs,msoverlap)));
+subplot(2,3,5),imagesc(synSpec); axis xy; colormap(1-gray);
+xlabel('Frame #');
+
+origloc = origSpec(:,frameNumber); origloc=origloc-max(origloc);origmin=min(origloc);
+subplot(2,3,3),plot(origloc),title('Spectrogram'),
+axis([1 length(origloc) origmin 0]);
+
+filloc = synSpec(:,frameNumber); filloc=filloc-max(filloc);
+subplot(2,3,6),plot(filloc);ylabel('db');
+axis([1 length(origloc) origmin 0]);
+xlabel('FFT Bin');
+
+function spec=lpc_spec(aCoeff)
+gain=0;
+cft=0:(1/255):1;
+for index=1:size(aCoeff,1)
+ gain = gain + aCoeff(index)*exp(-i*2*pi*cft).^index;
+end
+gain = abs(1./gain);
+spec = 20*log10(gain(1:128))';