how can i find zero crossing irregularity of the sound data?

Question

furkan akhan 2021-3-19

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/777667-how-can-i-find-zero-crossing-irregularity-of-the-sound-data

评论： Star Strider 2021-3-20

I have an sound file of an asthma patient. My aim here is to find the zero crossing irregularity of this sound file. Can you help me?

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Star Strider 2021-3-19

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/777667-how-can-i-find-zero-crossing-irregularity-of-the-sound-data#answer_652662

在 MATLAB Online 中打开

Find the zero-crossings using something like this approach:

[y,Fs] = audioread('Asthma.wav');                           % Import Sound File (Use The Correct File Name)
zxi = find(diff(sign(y(:,1))));                             % Approximate Indices Of Zero-Crossinga
ylen = numel(y(:,1));
tv = linspace(0, ylen, ylen).'/Fs;                          % Time Vector (Column Vector)
for k = 1:numel(zxi)
    idxrng = max(1,zxi(k)-2):min(ylen,zxi(k)+2);            % Index Range
    B = [t(idxrng) ones(size(idxrng))] \ y(idxrng);         % Parameters
    txz(k) = B(2)/B(1);                                     % Interpolated Precise Time Of Zero-Crossing
end

However, if you are doing time-frequency analysis of the wheezes, the Signal Processing Toolbox pspectrum function with the 'spectrogram' option is likely most appropriate. (The spectrogram function may also be useful, however the outputs are not as straightforward to interpret.)

6 个评论
显示 4更早的评论隐藏 4更早的评论

Star Strider 2021-3-20

First, I meant audioplayer not audioread in my earlier Comment.

I would have to have the structure array to experiment with to advise you on that.

Star Strider 2021-3-20

在 MATLAB Online 中打开

I would do something like this to do the time-frequency analysis:

W = load('sampleWheezeData.mat');
% LungSounds = W.structWheeze;
% SoundData = LungSounds.SoundData;
% Properties = LungSounds.Properties;
C1 = struct2cell(W.structWheeze);
Fs = 44100;                                                     % Not Provided, Wild Guess
for k = 1:size(C1,2)
    figure(k)
    pspectrum(C1{2,k}, Fs, 'spectrogram')
end

See the documentation on pspectrum for details on how to extract necessary information from it. I am not sure what sort of analysis you want to do with these data, however this should get you started.

This routine will extract the zero-crossing data:

for k1 = 1:size(C1,2)
    y = C1{2,k1};
    zxi = find(diff(sign(y(:,1))));                             % Approximate Indices Of Zero-Crossinga
    
    ylen = numel(y(:,1));
    tv = linspace(0, ylen, ylen).'/Fs;                          % Time Vector (Column Vector)
    
    for k2 = 1:numel(zxi)
        idxrng = max(1,zxi(k)-2):min(ylen,zxi(k)+2);            % Index Range
        B = [tv(idxrng) ones(size(idxrng)).'] \ y(idxrng);      % Parameters
        txz(k1,k2) = -B(2)/B(1);                                % Interpolated Precise Time Of Zero-Crossing
    end
end
figure
contourf(txz)
set(gca, 'YTick',1:5)