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David, that is very well stated and matches my concerns exactly. I don’t want a computer guessing what the notes are, or worse yet, what they ‘should’ be. This is why I’ve been asking what exactly is the ‘intelligence’ trying to do for us.


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Originally Posted by dcuny
In theory, if something is learnable, AI will eventually find a method of learning it.

For the purposes of AI training, audio needs to be converted into something that computers can deal with. That's typically FFT, which then is split into frequency bands to better simulate human hearing.

If that's the case, getting "missing" audio data and filling in holes is a matter of extrapolation for the neural network.
Who can deny that David always brings intelligent perspectives to any AI discussion?

The way that I understand "if something is learnable, AI will eventually find a method of learning it" is that human areas of study can be broken down into "languages". Be they formal languages like English, French, Fortran, music and math. Or areas of study like chemistry, physics, biology or engineering. Because the info and data of these languages can be encoded within the architecture and parameters of a neural net, an AI can learn it. Indeed, we all carry neural nets in our heads, of the biologic kind.

It's funny you mention FFT, in "another life" I actually programmed an FFT analyzer from scratch in Mathematica to process captured accelerometer data from a flight test I participated in over the Pacific Ocean at 37,000 ft. in a Boeing test aircraft. The final result was a set of broadband random vibration profiles that we could then run in our test lab . . . one of the most fun projects I ever participated in.

My idea for capturing the missing audio data (if even required) is to utilize a separate AI, perhaps one that uses more of an LLM approach. But so far, based on my very limited test cases, Studio One ver 7, doesn't seem to suffer with the "missing audio data phenomenon". But, who knows how well it will perform with music of higher complexity like that of Snarky Puppy and similar.

Attached is one slide of the slide-deck I'm working on. One point of this slide is to compare the 13,002 parameters of my simple example to the 24+ million parameters of Banquet to that of ChatGPT-4 where I've seen reported values of 1.8 trillion.

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Now that’s a great idea, using a Snarky Puppy track (hopefully not squashed). Their music has it all: great dynamic range, unexpected chords, sudden transitions etc. I played a song of theirs in a college concert where they brought me in to supplement the college music students. They ate that stuff up, and I struggled to keep up with the complexity, but I loved playing it.


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