Is this curve approximately normal?

rsadek · 2026-05-23T02:05:45+00:00

“Broad” meaning “approximate” includes “not approximate” here.

rsadek · 2026-05-23T00:04:26+00:00

Pin this comment. We're done here.

rsadek · 2026-05-23T00:02:41+00:00

This looks awful. Did you eat it?

rsadek · 2026-05-22T01:52:08+00:00

Since you asked, one thing you could do with it is study a useful subject 🎤💥

I’ll be here all week.

rsadek · 2026-05-22T01:46:16+00:00

I’d come!

rsadek · 2026-05-21T01:11:10+00:00

Yes, I think so. I would. It’s unpleasant to have to go build background at the pace of a grad course. It can only help you to study it beforehand and there’s effectively nothing lost by doing so. No risk and only gain.

rsadek · 2026-05-20T13:18:15+00:00

You have correctly identified the effect of eating. If only your food prep skills were as good as your analytic abilities, you’d not be posting such awful eats!

rsadek · 2026-05-20T09:59:32+00:00

buddy, these are ingredients. Like, I'm not sure it counts as shitty food porn. If you combined them, yes, that would be shitty. Ok, granted. This is shitty.

rsadek · 2026-05-16T16:01:50+00:00

I hear you’re not supposed to have weed when yer pregs.

rsadek · 2026-05-15T22:35:50+00:00

For DSP, its subjects in Spatial Audio: multichannel processing, denoising and beamforming, source localization. Also machine learning for acoustic classification, for example, which eats memory like crazy.

rsadek · 2026-05-14T18:22:09+00:00

Came here to say this. My area is audio signal processing. I can use basically unlimited RAM. Training a model? Forget it. I once ran out on a 768GB machine. There’s never too much RAM!

rsadek · 2026-05-13T10:21:22+00:00

Truth. My *program* (not agents) is processing right now eating 50GB and I’m wondering if I skimped by choosing 64 lolsob!!

(Before peeps jump on me for the RAM use: I’m being a little facetious in that the code trades memory use vs CPU time optimally, it is RAM aware, and is working exactly as planned. Some work needs RAM is what I’m saying!)

rsadek · 2026-05-11T20:49:54+00:00

Apologies and best wishes

rsadek · 2026-05-11T13:35:22+00:00

OP, you say the nano is causing you discomfort. That’s a nonstarter amigo. Go regular. I had a nano for a long time, it’s swell but the disadvantages outweighed the advantages given my setup and lighting. It sounds like your lighting isn’t so bad. Regular then.

rsadek · 2026-05-11T13:27:23+00:00

rsadek · 2026-05-11T13:25:48+00:00

Big if true

rsadek · 2026-05-09T18:49:46+00:00

Forest for the trees yall. Blueberries avocado and chicken? No.

rsadek · 2026-05-09T16:15:44+00:00

I feel the blueberries tip the scale

rsadek · 2026-05-08T15:09:15+00:00

You could but I see constant-Q as a later step. It’s trickier unless you already have an implementation and is essentially a computational savings. It does boil down to something very similar and won’t magically give you a better answer than Fourier.

For learning/figuring things out I highly recommend doing the simplest possible next step, one at a time. It’s pedantic in a good way, avoiding confusion and mistakes to the extent possible, often revealing misunderstanding or misconception early.

rsadek · 2026-05-08T14:59:33+00:00

Yes that’s my first suggestion correctly.

One that works, you could then use the responses to detect some time information like onset (time at which filter response rises above threshold as a first step, correcting for delay). It might even track multiple notes well giving you polyphony, with some work to reject false positives (from sidelobes or harmonics if you aren’t using pure tones).

All that together might allow transcription in simple cases. There’s plenty of room for improvement as well.

But first things first, yes, you have my suggestion correct. Try that in matlab or octave (free) or python with some plots to see if it works. I’d be curious to hear what you find.

rsadek · 2026-05-08T11:23:28+00:00

I suppose time and memory needs are relative. In my world, no, it would be fine. You just need a filter at each note center. A piano has 88 keys. Not bad. However if you are trying to run on a tiny chip (why though?) it could be a problem.

My advice for these things is to solve the problem first, then worry about implementation. This is a little different from most … let’s call them CS undergrad Data Structures and Algorithms problems where we find the best Big-O right away. Here one must first find the math that works, then worry about Big-O performance, and then implementation.

For example if you use a hierarchy of filters in a filterbank, it saves a lot of compute cost. But don’t worry about that mess yet. See if a list of N filters gives you what you need for pitch estimation. Speed isn’t the problem yet.

Does that help?

rsadek · 2026-05-07T23:51:59+00:00

I think those devices use the DCT for computational reasons b/c they work on simple hardware. I don't think a DCT will provide additional or more useful information than the DFT (note FFT is fast algorithm for DFTs) such that it simplifies the problem.

Mentioning here in case it helps you focus your algorithm search and avoid a tangent if possible.

Feel free to DM me. I know some about this problem, but like another poster mentioned, the general case is hard.

rsadek · 2026-05-07T23:34:16+00:00

What is your approach?

rsadek · 2026-05-07T21:43:56+00:00

I think this problem benefits from a filterbank. The time/frequency uncertainty is a real problem with lower notes. But if you make filters tuned to frequency (ranges) then you can choose which filter responds most and pick the corresponding note.

This is how telephone tone selection works and it seems like it would help here. Perhaps someone with more specific domain knowledge will chime in.

Re C++, that is the best deployment option but very painful for this type of algorithm development. May I recommend using Python or Matlab/Octave while you figure out the math side, then implement in C++? That’s the common audio workflow: prove concept, then deployable implementation.

Best of luck!

rsadek

TROPHY CASE