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1 function varargout = mirrolloff(x,varargin)
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2 % r = mirrolloff(s) calculates the spectral roll-off in Hz.
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3 % Optional arguments:
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4 % r = mirrolloff(s,'Threshold',p) specifies the energy threshold in
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5 % percentage. (Default: .85)
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6 % p can be either a value between 0 and 1. But if p exceeds 1, it
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7 % is understood as a percentage, i.e. between 1 and 100.
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8 % In other words, r is the frequency under which p percents
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9 % of the spectral energy is distributed.
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10 %
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11 % Typical values for the energy threshold:
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12 % 85% in G. Tzanetakis, P. Cook. Musical genre classification of audio
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13 % signals. IEEE Tr. Speech and Audio Processing, 10(5),293-302, 2002.
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14 % 95% in T. Pohle, E. Pampalk, G. Widmer. Evaluation of Frequently
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15 % Used Audio Features for Classification of Music Into Perceptual
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16 % Categories, ?
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17
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18 p.key = 'Threshold';
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19 p.type = 'Integer';
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20 p.default = 85;
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21 p.position = 2;
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22 option.p = p;
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23
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24 specif.option = option;
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25
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26 varargout = mirfunction(@mirrolloff,x,varargin,nargout,specif,@init,@main);
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27
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28
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29 function [s type] = init(x,option)
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30 s = mirspectrum(x);
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31 type = 'mirscalar';
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32
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33
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34 function r = main(s,option,postoption)
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35 if iscell(s)
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36 s = s{1};
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37 end
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38 m = get(s,'Magnitude');
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39 f = get(s,'Frequency');
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40 if option.p>1
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41 option.p = option.p/100;
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42 end
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43 v = mircompute(@algo,m,f,option.p);
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44 r = mirscalar(s,'Data',v,'Title','Rolloff','Unit','Hz.');
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45
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46
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47 function v = algo(m,f,p)
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48 cs = cumsum(m); % accumulation of spectrum energy
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49 thr = cs(end,:,:)*p; % threshold corresponding to the rolloff point
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50 v = zeros(1,size(cs,2),size(cs,3));
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51 for l = 1:size(cs,3)
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52 for k = 1:size(cs,2)
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53 fthr = find(cs(:,k,l) >= thr(1,k,l)); % find the location of the threshold
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54 if isempty(fthr)
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55 v(1,k,l) = NaN;
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56 else
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57 v(1,k,l) = f(fthr(1));
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58 end
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59 end
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60 end |
