Things like this are why I go on the internet.
Cat meow sounds visualized with auto-correlation function
11–20 of 35 posts
Re: Cat meow sounds visualized with auto-correlation function
#12> 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).
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)?
Re: Cat meow sounds visualized with auto-correlation function
#13Not sure how these images are being generated but in my experience there is a wide variety of purr and meow. Can't say unique because I have no way of knowing.
Cats are magical to me but in reality I can be friends with all sorts of animals, even people!
Re: Cat meow sounds visualized with auto-correlation function
#14> 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). 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 )?
I might be mistaken but since the auto-correlation function is the inverse FFT of the power spectral density and power spectral density doesn't contain information about the phase. Thus, it's like dropping the phase and taking IFFT(|A|^2)
Re: Cat meow sounds visualized with auto-correlation function
#15Well, I guess this one is never going to be more relevant: https://xkcd.com/26
Re: Cat meow sounds visualized with auto-correlation function
#16Is "dropping the phase" the same thing as computing the spectral power distribution? P.S. A4 = 432Hz is a stupid fad that can't die soon enough.
To quote the dude, "that's like your opinion, man."
For others less familiar with this poster's preference and their angst: https://producerhive.com/editorial/432hz-vs-440hz/
Re: Cat meow sounds visualized with auto-correlation function
#17> 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). 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 )?
Wikipedia is great at obfuscating simple ideas in complex math. The "Efficient computation" explains the idea well, but it could be made even simpler. The amplitude squaring step drops the phase there.
Re: Cat meow sounds visualized with auto-correlation function
#18Is "dropping the phase" the same thing as computing the spectral power distribution? P.S. A4 = 432Hz is a stupid fad that can't die soon enough.
Seems so, except that in this case it is basically just the modulus whereas the PSD is the modulus squared |A|^2
Re: Cat meow sounds visualized with auto-correlation function
#19Well, I guess this one is never going to be more relevant: https://xkcd.com/26
The xkcd is half-right. Cats are ACFs, not FFTs, because they are even functions in polar coordinates, I mean the left half of the cat mirrors the right half, and the two halves merge nicely, without discontinuities. I probably don't want to know what cats look like with the phase components restored.
Re: Cat meow sounds visualized with auto-correlation function
#20Hi HN! 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 th…
I would think that the absolute notes of the sounds are not the relevant metric (that was colored here). I naively imagine it is the relative tonal structure. That is, meaning does not come from particular frequencies, but from the relationship of the frequencies. I wonder, if so, how that might be represented and normalized. Just like we can understand two people speaking with different basic pitches, then add meaning when they add shifts from those basic pitches.
Either way super pretty visualization!