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Cough_Geek OP t1_j7kucf7 wrote

[OC] audio collected through a smartphone cough monitoring application. Audio to visual scalogram generated in R (wavelet transform), decomposing the signal into frequencies. Purple background allows to see where the explosive peak of a cough sound happens, with the vocal phase of a cough following as trailing bright spots.

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joev714 t1_j7lseby wrote

It most likely doesn’t matter, but which wavelet did you use

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Cough_Geek OP t1_j7lylto wrote

Thanks for this great question - complex-valued Mexican hat mother wavelet here, used this implementation of synchrosqueezing: https://github.com/OverLordGoldDragon/ssqueezepy

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mrbrambles t1_j7mble9 wrote

What is your goal in processing? I used to do a lot of image processing with wavelets, so curious on how much is applicable knowledge here. In images, edge/feature detection and smoothing are some of the main goals.

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cyan-pink-duckling t1_j7nmi4r wrote

For starters, this can be an efficient way to set up voice activation. Sound signals are generally temporally compact, and somewhat sparse in Mexican hat basis.

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Baixst t1_j7m7fav wrote

I would like to see some labels on both axis, especially the time-axis. I found the scalogram to be problematic for audiosignal that are a few seconds long.

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Cough_Geek OP t1_j7ma19d wrote

Agree with labels, that would look more professional, in this case - thought of sharing it as a piece of art. By the way - thanks so much for giving an elaborate reply on the differences in colours - very much appreciate that!

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mrbrambles t1_j7mat01 wrote

Why not a Fourier transform? I’m many years out of practice but is a wavelet transform just for computational speed or are you specifically trying to accentuate certain qualities?

Edit read your other reply - nice

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