Show HN: YouTube Musical Spectrum – visualizer for YouTube with musical notes
github.com
github.com
It's just too bad this is a browser extension because I really don't want more of those. I get that the economics probably don't make sense to do any more, given the effort to make a standalone desktop app would be a lot and a website people could upload tracks to in order to get a visualization would force you to pay hosting costs.
Would also be cool if the dots on the pitch line that represent accidentals were perhaps a slightly darker shade so it'd be easier to see the typical piano pattern in them. (Like ⦾⦿⦾⦿⦾⦾⦿⦾⦿⦾⦿⦾ kinda thing.)
Anyway, to original poster: very cool extension, thanks for sharing! :)
> As someone who has been learning how to sing for the past year, I have tried many spectrum tools and have come across one that is really useful for singing with my own voice but isn't that great for multi-frequency visualization and therefore isn't that good for learning other songs but THIS extension really fills a void for learning other songs!!
I like how I can tell the voice from the instruments in this song by the vibrato visualization: https://www.youtube.com/watch?v=450p7goxZqg
Also have you considered coloring the spectrum according to the musical note? So every C is red, every E is green, every G is cyan, etc.
Because AV1's decoding is slow and on some low-end hardware it makes the animation stutter.
> Also have you considered coloring the spectrum according to the musical note? So every C is red, every E is green, every G is cyan, etc.
The color is used for left-right channel separation.
Does anyone know any stand alone feature rich audio visualizer for Windows? I am surprised that Spotify never included any visualizer for the desktop client. I miss the old Winamp days.
I haven't been able to find a proper way to set up mesh size on windows but if you're running it on linux or android, those options are available too.
So in what way is it variable Q? I thought the innovation of CQT over a regular FFT is that the bins represent each note step with the same 'Q', making notes more distinguishable? (whereas a FFT has more 'Q' in the high frequencies)
I think having an autocorrelation option on the visualization could be cool, as it could reduce the spikes from the overtones (and also show a missing fundamental). But I think you'd need a different convolution for each instrument.
CQT's window length in time domain = k/f
showcqt-js window length in time domain = a * b / (a / c + b * f / (1 - c)) + a * b / (b * f / c + a / (1 - c))
whera a = 384, b = 0.33, c = 0.17
I also apply asymmetric window to reduce latency before doing VQT.
> I think having an autocorrelation option on the visualization could be cool, as it could reduce the spikes from the overtones (and also show a missing fundamental). But I think you'd need a different convolution for each instrument.
Research needed. Doing autocorrelation on simple single instrument monophonic audio is probably easy. But doing it on complex multi instrument audio isn't easy. Probably, it needs some sort of machine learning.
> whera a = 384, b = 0.33, c = 0.17
So this is some sort of compromise to increase speed? Have you thought about implementing in WASM?
> Probably, it needs some sort of machine learning.
Agreed, but for a visualisation, it could just be a parameter just for the user to mess with.
Partially. The main purpose is to increase time domain accuracy on low frequency. If you want to do experiment on window length (in time domain), use ffmpeg showcqt filter (https://ffmpeg.org/ffmpeg-filters.html#showcqt). showcqt-js window length is hardcoded to tc=0.33 attack=0.033 and tlength='st(0,0.17); 384tc / (384 / ld(0) + tcf /(1-ld(0))) + 384tc / (tcf / ld(0) + 384 /(1-ld(0)))' (https://github.com/mfcc64/mpv-scripts/blob/a0cd87eeb974a4602...).
> Have you thought about implementing in WASM?
It is implemented in WASM.
> Agreed, but for a visualisation, it could just be a parameter just for the user to mess with.
If you want to do experiment on it, showcqt.js exposes intermediate color data cqt.color[]. It can be modified arbitrarily, including autocorrelation. If some day I do experiment on it and find that the result is satisfying, maybe I'll include it in YouTube Musical Spectrum.
I thought so. The main issue with regular CQT is latency on low frequencies due to the excessive window required. It makes it unsuitable for real- time applications, as you know. Thanks for the insights