Cat meow sounds visualized with auto-correlation function(soundshader.github.io) |
Cat meow sounds visualized with auto-correlation function(soundshader.github.io) |
I used the meow sounds from https://soundspunos.com/animals/10-cat-meow-sounds.html. I expected to see very little variability in the meows, maybe just 4-5 different types for basic emotions. To my surprise, each “cat meow” has astonishingly colorful, complex and unique structure, unlike human vowels that follow a more or less predictable pattern: https://soundshader.github.io/vowels.
The algorithm behind these images is fairly simple. It computes FFT to decompose the sound into a set of A·cos(2πwt+φ) waves and drops the phase φ to align all cos waves together. This is known as the auto-correlation function (ACF). Before merging them back, it colorizes each wave using its frequency w: the A notes (432·2ⁿ Hz) become red, C notes - green, E notes - blue, and so on. Finally, it merges the colored and aligned cos waves back, using the amplitude A for color opacity, and renders them in polar coordinates, where the radial coordinate is time.
I'm guessing some kind of overtone structure in these sounds (perhaps decipherable to cats, but not to us)?
I await your insight.
I would love to see more examples across species and variants of felines, under controlled conditions. Could you figure out an appropriate color map?
Not to start a dog vs cat war, but as someone who loves both, I think I can safely say that cats put much more information in their voices than dogs, for example.
1. Time progresses from the center to the edge of the circle.
2. Color means note, e.g. A4=432Hz is red, but so is A1, A2 and all other A notes. B is orange, C is yellow, D is green and so on.
3. The amount of fine details is frequency: the higher the frequency, the more fine details you see. If notes of different colors and different frequencies sound simultaneously, e.g. a A2 with a G5, you’ll see a red belt with a few repetitions mixed with a blue belt with 8x more repetitions, so the result will be a purple belt with a fine structure.
For example, on one image below there is a green belt with 10 repetitions. One repetition correponds to 13.5 Hz here (55296 Hz sample rate, 4096 FFT bins), so 10 repetitions is 135 Hz, which corresponds to C3. On another image there is a curious red cross in the center, it’s a red belt with 2 repetitons. That’s 27 Hz, or A0, almost infrasound.
Either way super pretty visualization!
I don't see that. And without this, the effect is ... just some pretty pictures.
https://soundshader.github.io/?n=4096&img=2048&acf.lr=5&sr=5...
P.S. A4 = 432Hz is a stupid fad that can't die soon enough.
> rounding 432 to 440
Rounding from what to what? Why does the number cycles relative to seconds being any particular number matter?
For others less familiar with this poster's preference and their angst: https://producerhive.com/editorial/432hz-vs-440hz/
Cats are magical to me but in reality I can be friends with all sorts of animals, even people!
How is simply dropping the phase transforming FFT into ACF (according to various definitions of ACF as shown here: https://en.wikipedia.org/wiki/Autocorrelation)?
On the other hand, 440 Hz seems just a random number to me picked by someone with little imagination.
My cat only gets really vocal when she wants something ( & usually only with me, not my kids or spouse). She also trills a lot, usually in surprise. Could be they compress the data - dogs are very chatty - maybe the cats focus on high throughput whereas the dogs go for low-latency.
I'd love to see the same analysis done with dog barks.
I really like that way of thinking about it. Cats hit the low latency pretty quick with the hissing, which is what the wild cats looked more like in this projects demo. Kinda makes sense.
But you obviously know more about this than me, I'm just putting out what I know. Your paragraph above, I actually copied so I can study it a few times. So thanks.