21–30 of 41 posts
Could someone fill me in why would machine learning be necessary for pitch detection? Isn't it something that could just be solved with FFT or it's a much more complicated task?
Earlier quoted context omitted.
Can you not just write it from the paper(s)? Or is that more effort than value to you? > that translating eg. GPL Python or C++ into C would mean the license is retained It depends a bit on what exactly "translating" means but you could easily be a derivative work. Honestly in that situation I wouldn't even look at the code. You might use in to test equivalent behavior after you have your own implementation, but only…
I think I have to look at the code when using other people's MIT licensed code... If they have used something that's GPL or used someone else's code that turns out to be GPL, then it becomes my problem when translating it. And I'm not smart enough to just follow a paper
Don't sell yourself short. This is the sort of thing that is only straightforward if you have the right background.
What are your thoughts on PESTO which learns pitch-prediction very well with a small network, and uses a self-supervised objective?
To the dev: the tuner gives me an incredibly high error window with the following message. It doesn't prompt to access the mic (I think that's related). Ubuntu/KDE/Firefox: An error occurred running the Unity content on this page. See your browser JavaScript console for more info. The error was: TypeError: 'microphone' (value of 'name' member of PermissionDescriptor) is not a valid value for enumeration PermissionNam…
Thank you for providing error feedback. We will work hard to address it. Currently, the model-related data is relatively large, which may be related to network speed.
Could someone fill me in why would machine learning be necessary for pitch detection? Isn't it something that could just be solved with FFT or it's a much more complicated task?
Pitch is a *subjective* property, inherently tied to the complex processing humans use to perceive sounds. “Simple” physical measures like fundamental frequency of a periodic signal are very closely related, but for real-world audio (aren’t really periodic), the relationship is more complicated.
Earlier quoted context omitted.
Pitch is a *subjective* property, inherently tied to the complex processing humans use to perceive sounds. “Simple” physical measures like fundamental frequency of a periodic signal are very closely related, but for real-world audio (aren’t really periodic), the relationship is more complicated.
Could you elaborate a bit more? It seems to me like the note being played would always correspond to the fundamental frequency observed. When is this not the case? Maybe as the note rings out, the fundamental frequency and first few overtones lose power, and all that's still audible are the higher overtones?
Does anyone know where I should look if I want to detect specific sounds? Like a smoke alarm, food bowl dispenser (its very distinct), cat meowing, 3d printer collision, that sort of thing?
Does anyone know where I should look if I want to detect specific sounds? Like a smoke alarm, food bowl dispenser (its very distinct), cat meowing, 3d printer collision, that sort of thing?