EMD分解语音信号求近似熵

zhendongbang

哪位大神会近似熵的程序啊，对已经分解的imf分量（EMD分解）求近似熵，求好心人帮帮忙，有酬谢哦...

chybeyond

百度搜一下很多程序http://chainaren2008.blog.163.co ... 101920113383349577/

zhendongbang

这个程序我试过，有错，调试过也不行，而且程序不全，后面应该还有几句

chybeyond

zhendongbang 发表于 2014-5-4 10:38
这个程序我试过，有错，调试过也不行，而且程序不全，后面应该还有几句

1. function [apen] = approx_entropy(n,r,a)
   
2. 
   
3. 
   
4. %% Code for computing approximate entropy for a time series: Approximate
   
5. % Entropy is a measure of complexity. It quantifies the unpredictability of
   
6. % fluctuations in a time series
   
7. 
   
8. % To run this function- type: approx_entropy('window length','similarity measure','data set')
   
9. 
   
10. % i.e  approx_entropy(5,0.5,a)
    
11. 
    
12. % window length= length of the window, which should be considered in each iteration
    
13. % similarity measure = measure of distance between the elements
    
14. % data set = data vector
    
15. 
    
16. % small values of apen (approx entropy) means data is predictable, whereas
    
17. % higher values mean that data is unpredictable
    
18. 
    
19. % concept boorowed from http://www.physionet.org/physiotools/ApEn/
    
20. 
    
21. % Author: Avinash Parnandi
    
22. %%
    
23. 
    
24. 
    
25. data =a;
    
26. 
    
27. 
    
28. for m=n:n+1; % run it twice, with window size differing by 1
    
29. 
    
30. set = 0;
    
31. count = 0;
    
32. counter = 0;
    
33. window_correlation = zeros(1,(length(data)-m+1));
    
34. 
    
35. for i=1:(length(data))-m+1,
    
36.     current_window = data(i:i+m-1); % current window stores the sequence to be compared with other sequences
    
37.    
    
38.     for j=1:length(data)-m+1,
    
39.     sliding_window = data(j:j+m-1); % get a window for comparision with the current_window
    
40.    
    
41.     % compare two windows, element by element
    
42.     % can also use some kind of norm measure; that will perform better
    
43.     for k=1:m,
    
44.         if((abs(current_window(k)-sliding_window(k))>r) && set == 0)
    
45.             set = 1; % i.e. the difference between the two sequence is greater than the given value
    
46.         end
    
47.     end
    
48.     if(set==0)
    
49.          count = count+1; % this measures how many sliding_windows are similar to the current_window
    
50.     end
    
51.     set = 0; % reseting 'set'
    
52.    
    
53.     end
    
54.    counter(i)=count/(length(data)-m+1); % we need the number of similar windows for every cuurent_window
    
55.    count=0;
    
56. i;
    
57. end  %  for i=1:(length(data))-m+1, ends here
    
58. 
    
59. 
    
60. counter;  % this tells how many similar windows are present for each window of length m
    
61. %total_similar_windows = sum(counter);
    
62. 
    
63. %window_correlation = counter/(length(data)-m+1);
    
64. correlation(m-n+1) = ((sum(counter))/(length(data)-m+1));
    
65. 
    
66. 
    
67. end % for m=n:n+1; % run it twice   
    
68.    correlation(1);
    
69.    correlation(2);
    
70. apen = log(correlation(1)/correlation(2));
    
71.    
    

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没用过近似商，这个是别人写的一个程序，你试试吧，

zhendongbang

等待验证中....

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