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1 function [output,aCoeff] = WhiteVowel(data,sr,L,pos)
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2 % function [output,aCoeff] = WhiteVowel(data,sr,L,pos)
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3 %
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4 % Speech is often described as having spectral peaks or formants which
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5 % identify the phonetic signal. An interesting experiment, first proposed by
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6 % XXX, filters a speech signal to remove all the formant information at one
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7 % time during the speech. If there are no formant peaks, how can the speech
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8 % be understood? It turns out that processing, much like RASTA, means that
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9 % relative changes in spectrum are the most important, thus the speech signal
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10 % is understood because the formant transitions carry the information. This
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11 % gives speech an important transparency due
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12 %
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13 % This function takes a speech signal (data) with a given sampling rate (sr).
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14 % It then finds the L-order LPC filter that describes the speech at the given
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15 % position (pos ms). The entire speech signal is then filtered with the
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16 % inverse of the LPC filter, effectively turning the speech spectrum at the
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17 % given time white (flat).
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18
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19 % Chris Pal, Interval, May 1997
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20 % (c) 1998 Interval Research Corporation
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21
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22 fr = 20; fs = 30; preemp = .9378; % LPC defaults
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23
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24 [row col] = size(data);
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25 if col==1 data=data'; end
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26
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27 nframe = 0;
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28 msfr = round(sr/1000*fr);
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29 msfs = round(sr/1000*fs);
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30 duration = length(data);
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31 msoverlap = msfs - msfr;
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32 frameNumber = floor(pos/1000*sr/msfr);
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33
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34 frameStart = round(pos/1000*sr - msfs/2);
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35 frameData = data(frameStart:(frameStart+msfs-1));
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36 aCoeff = proclpc(frameData, sr, L, fr, fs, preemp);
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37 % Calculate the filter response
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38 % by evaluating the z-transform
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39 spec=lpc_spec(aCoeff);
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40 subplot(2,3,1);
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41 plot(spec);
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42 title('LPC Spectral Slice');
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43 ylabel('Original')
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44
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45 % Now do the actual whitening filter
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46 output = filter(aCoeff,1,data)';
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47
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48 frameData = output(frameStart:(frameStart+msfs-1));
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49 bCoeff = proclpc(frameData, sr, L, fr, fs, preemp);
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50 spec=lpc_spec(bCoeff);
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51 subplot(2,3,4);
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52 plot(spec);
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53 ylabel('Whitened'); xlabel('FFT Bin');
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54
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55 % 256-DFT
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56 origSpec = 20*log10(abs(specgram(data,512,sr,msfs,msoverlap)));
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57 subplot(2,3,2),imagesc(origSpec); axis xy; colormap(1-gray);
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58 title('Spectrogram');
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59
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60 synSpec = 20*log10(abs(specgram(output,512,sr,msfs,msoverlap)));
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61 subplot(2,3,5),imagesc(synSpec); axis xy; colormap(1-gray);
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62 xlabel('Frame #');
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63
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64 origloc = origSpec(:,frameNumber); origloc=origloc-max(origloc);origmin=min(origloc);
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65 subplot(2,3,3),plot(origloc),title('Spectrogram'),
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66 axis([1 length(origloc) origmin 0]);
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67
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68 filloc = synSpec(:,frameNumber); filloc=filloc-max(filloc);
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69 subplot(2,3,6),plot(filloc);ylabel('db');
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70 axis([1 length(origloc) origmin 0]);
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71 xlabel('FFT Bin');
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72
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73 function spec=lpc_spec(aCoeff)
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74 gain=0;
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75 cft=0:(1/255):1;
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76 for index=1:size(aCoeff,1)
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77 gain = gain + aCoeff(index)*exp(-i*2*pi*cft).^index;
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78 end
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79 gain = abs(1./gain);
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80 spec = 20*log10(gain(1:128))';
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