[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.

Pretty looking thing. Do the different colors mean anything?

Very interesting…

I believe that color represents loudness, he X axis time and Y frequeny.

Is the "lonely" supposed to be "lowly"?

(Since you seem to be contrasting it with "rich".)

Good one haha! For a cough, that is lonely and rich - it is likely just a matter of time for another one to appear; then they are counted and depicted as cough bursts!

This looks exactly like some of my x-Ray spectroscopy data I studied in graduate school. Uncanny. Take a line out of this using ImageJ and analyze it!

Interesting - do you have some image to share which resembles cough's scalogram, just out of curiosity?

Hard to find a solid link that isn’t paywalled (most peer reviewed work is). You can google “x-ray spectroscopy z-pinch film UNR” and check out images. Won’t look as pretty as yours, our film was black and white.

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

Every time I shift my focus to a different part of this image, the periphery looks like it's shrinking. So much so, that I had to check if it was a GIF or a video.

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Impressive effect, I agree! Better be aware of those coughs and their dynamics irl before it gets worse!

Am I the only one who sees this moving? Coulda swore I was watching a video…

This is so cool! There was another user mentioning the same effect!

I just remembered I did microdose today so maybe that might be part of it 😅

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Thanks for sharing, will take a look!

u/Cough_Geek do they come in any other colours?

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

I did some more digging, NASA has a nice little website that if you scroll down you can see some nice spectra: https://imagine.gsfc.nasa.gov/educators/lessons/xray_spectra/background-spectroscopy.html

To tell you the truth - coughs are super colourful! Usually people are not used to pay attention to cough in general, and this is about to change big time! And in regards to the visual - another example in different colours here: https://ibb.co/7VXJm79

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The scalorgram shows the power distribution over time and frequency of a given signal. So in a sense you can say that the colors represente how strongly a frequency (y-axis) is present at a point in time (x-axis).

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.

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!

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

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.

very cool!

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Ooh pretty colors. Doesn't mean anything since nothing is labeled, but I suppose you could try to sell it to a modern art museum

>DataIsBeautiful is for visualizations that effectively convey information. Aesthetics are an important part of information visualization, but pretty pictures are not the sole aim of this subreddit.

Meanwhile in r/dataisbeautiful : a random scalogram of a cough audio recording with no title, no axes labels, no color bar, big ass empty margins and zero annotations or description what so ever gets hundreds of upvotes ...

Looks like a big dick and balls

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.

I appreciate your feedback very much - as work on cough analysis continues, all following interesting visuals will come addressing your points!