Live data from Hacker News

How the cochlea computes (2024)

dissonances.blog

1–10 of 159 posts

Re: How the cochlea computes (2024)

#4
FT is frequency domain representation.

neural signaling by action potential, is also a representation of intensity by frequency.

the cochlea is where you can begin to talk about bio-FT phenomenon.

however the format "changes" along the signal path, whenever a synapse occurs.

Re: How the cochlea computes (2024)

#5
post #3

Man, I've been spreading disinformation for years.

the closest i have been, was acoustic phase discrimination by owls.

there appears to be no software for this, its all hardware, the signal format flips as it travels through the anatomy.

Re: How the cochlea computes (2024)

#6
Nit: It’s an unfortunate confusion of naming conventions, but Fourier Transform in the strictest sense implies an infinite “sampling” period, while the finite “sample” period counterpart would correspond to Fourier Series even though we colloquially refer to them interchangeably.

(I had put “sampling” in quotes as they’re actually “integration period” in this context of continuous time integration, though it would be less immediately evocative of the concept people are colloquially familiar with. If we actually further impose a constraint of finite temporal resolution so that it is honest-to-god “sampling” then it becomes Discrete Fourier Transform, of which the Fast Fourier Transform is one implementation of.)

It is this strict definition that the article title is rebuking, but it’s not quite what the colloquial usage loosely evokes in most people’s minds when we usually say Fourier Transform as an analysis tool.

So this article should have been comparing to Fourier Series analysis rather than Fourier Transform in the pedantic sense, albeit that’ll be a bit less provocative.

Regardless, it doesn’t at all take away from the salient points of this excellent article which are really interesting reframing of the concepts: what the ear does mechanistically is applying a temporal “weigting function” (filter) so it’s somewhere between Fourier series and Fourier transform. This article hits the nail on the head on presenting the sliding scale of conjugate domain trade offs (think: Heisenberg)

Re: How the cochlea computes (2024)

#7
post #2

Tbh I used to think that it does. For example, when playing higher notes, it's harder to hear the out-of-tune frequencies than on the lower notes.

I haven't noticed that effect, to be honest. Actually I think its the really low bass frequencies that are harder to tune- especially if you remove the harmonics and just leave the fundamental.

Are you perhaps experiencing some high frequency hearing loss?

Re: How the cochlea computes (2024)

#9
post #2

Tbh I used to think that it does. For example, when playing higher notes, it's harder to hear the out-of-tune frequencies than on the lower notes.

I haven't noticed that effect, to be honest. Actually I think its the really low bass frequencies that are harder to tune- especially if you remove the harmonics and just leave the fundamental. Are you perhaps experiencing some high frequency hearing loss?

It's even more complex than that. The low notes are hard to tune because the fundamentals are very close to each other and you need to have super good hearing to match the beats, fortunately they sound for a long time so that helps. Missing fundamentals are a funny thing too, you might not be 'hearing' what you think you hear at all! The high notes are hard to tune because they sound very briefly (definitely on a piano) and even the slightest movement of the pin will change the pitch considerably.

In the middle range (say, A2 through A6) neither of these issues apply, so it is - by far - the easiest to tune.

Post reply on HN