I love the fact that TFA essentially re-created the functionality of an oscilloscope. Except, much more interesting to look at, and clearly shows an understanding of FFT math that I sometimes think I understand but then read stuff like this and remember I don't really.

Here's an example of using the O'scope: https://www.youtube.com/watch?v=19jv0HM92kw

You can make an affordable sound visualizer out of a tea cup, used speaker, led light and duct tape. Attach the tea cup to the speaker, add some water, drop a submersible led light and turn on the speaker. The vibration will produce patterns on the water that will refract the light onto the ceiling. With a decent quality setup, you'll see a mesmerizing quality projection of a 3d shape. This trick is used by dolphins to encode visuals in sound, and I've seen a speculation that thoughts in your imagination have the same nature.
I think i prefer spectrograms. Particularly for speech, where you can, after a while, almost read them.
I have this exact same idea on my audio synthesis and DSP exploration project called Akasha, that I have developed on and off for more than 13 years. I have plans to open source at least parts of it, once I get to clean up the code. I was inspired by Julius O. Smith III's writings on DSP algorithms: https://ccrma.stanford.edu/~jos/

Unfortunately my web pages are not online, I have some video samples of Björk, Gotan Project and some classical and electronic music samples.

In the analytical audio signal, the musical intervals can sometimes be easily seen, and it is mesmerizing to look at.

On this video demo by OP, when the waveform appears stationary with loops, there are intervals in (at least close to) just intonation – meaning the frequencies have integer ratio relationships.


the code is open source. I did a but of work a few months back updating it and fixing compilation with rust stable toolchain: https://github.com/khimaros/audioscope
I wonder if the author had ever tried milkdrop before? Seems like the same type of thing. https://en.m.wikipedia.org/wiki/MilkDrop
I found this mesmerizing. It conveys surprisingly many of the perceptual characteristics of the sound through visuals. Good demo music, too, but it makes me curious to see what it would do with vocals or acoustic music. Any other demos?

Not sure about the colors… they were hard to make sense of. And I wonder if it’s making use of stereo information at all.

Hey folks, I wrote this article! If you're interested in a simpler reference implementation, I made it in Web Audio: https://github.com/conundrumer/visual-music-workshop/blob/7b...

I've also continued developing new audio visualizers, like this one inspired by sands on a vibrating plate: https://twitter.com/conundrumer/status/1482615130185768961

Interesting, and I'd love to see some more demos on "traditional" music (Symphonic/Orchestral, Jazz, Beatles, etc.). I'm sure the the highly synthetic and "pure" music used in the demo video sure leads to a fairly coherent visualization as compared to more natural/acoustic sources of sound.

Also, what about any stereo components? Capturing that in the visualization would also be nice.

Possibly worth mentioning that this is from 2016.
There's a Google Experiment from 2018 using Hilbert Scopes:


Does anyone know of any chanting apps to give visual feedback on your voice as a tool for meditation?

Ooh, intriguing.

I've been using Flux studio session analyser to visualise audio signals for mixing In-ear monitors for live audio[1] from my Cue mix.

Might write a quick dodgy thing to add this to my analysis stack. Could be cool.

[1] https://imgur.com/RFsV3d9

If one is interested in audio visualisations, you should check out Cycling74's Max MSP with Jitter. That program has huge capabilities in making these things!
Would like to feed this voice samples, cat's meows, birdsongs, and laughter. Maybe the same voice in different emotional states. Looks fun to play around with.
Love this. Many hardware oscilloscopes can (and were) configured to do this in polar plotting mode.
Need winamp plug-ins!