1
$\begingroup$

I am using the following code in Matlab, from some source (sadly I cant remember).
The code is used for smoothing signals using a low pas filter.

 function y = fftSmooth(resp,srateCorrectedSmoothedWindow) L = length(resp); window = zeros(1,L); window(floor((L-srateCorrectedSmoothedWindow+1)/2)... :floor((L+srateCorrectedSmoothedWindow)/2))=1; % zero phase low pass filtering tmp = ifft(fft(resp).*fft(window)/srateCorrectedSmoothedWindow); y=-1*ifft(fft(-1*tmp).*fft(window)/srateCorrectedSmoothedWindow);

I have removed some unnecessary parts to shorten the code.

My question: how exactly is the filter designed (the last two lines)? Shouldn't it be in the time domain?

Improve this question
$\endgroup$
2
  • 1
    $\begingroup$ I think what you mean is "how is the filtering implemented" (which is different from how the filter is designed). The answer to that is it's implemented in the frequency domain, which is perfectly fine. I wouldn't have implemented it as such though, but that's a different topic. $\endgroup$ Commented Oct 18, 2022 at 18:10
  • 1
    $\begingroup$ See also: this $\endgroup$ Commented Oct 18, 2022 at 18:13

1 Answer 1

Reset to default
2
$\begingroup$

The code apparently originate from BreathMetrics (Matlab toolbox for analyzing respiratory recordings): breathmetrics/fftsmooth.m. It is descripted in Automated analysis of breathing waveforms using BreathMetrics: A respiratory signal processing toolbox, Chemical Senses, 2018. A draft is here.

Below, the function is visualyy decomposed into frequency/time steps. As @Jdip wrote, that is a filter implementation in the frequency domain.

%%% function y = fftSmooth(resp,srateCorrectedSmoothedWindow) %Set inputs lResp = 2048; resp = randn(1,lResp)+10*exp(-(((1:lResp)-lResp/2)/(0.01*lResp)).^2); srateCorrectedSmoothedWindow = 16; L = length(resp); window = zeros(1,L); window(floor((L-srateCorrectedSmoothedWindow+1)/2)... :floor((L+srateCorrectedSmoothedWindow)/2))=1; % zero phase low pass filtering tmp = ifft(fft(resp).*fft(window)/srateCorrectedSmoothedWindow); y=-1*ifft(fft(-1*tmp).*fft(window)/srateCorrectedSmoothedWindow); figure(1);clf subplot(2,1,2) plot([resp;y]') axis tight;legend('Time Signal','Time Signal filtered') subplot(2,1,1) yyaxis left plot(abs(fft(resp))) yyaxis right plot(abs(fft(window))) axis tight;legend('Spectrum','Window')

Decomposition of algorithm

Improve this answer
$\endgroup$

Start asking to get answers

Find the answer to your question by asking.

Ask question

Explore related questions

See similar questions with these tags.