We’re announcing something new today: Motif - music that moves with your yoga class.
Instead of static playlists, the music follows the structure and energy of a class and adapts on the fly.
Made a rather serviceable timestretching algo for drum loops with Fable, some web research, and a few rounds of listening.
Cut the loop at every hit, keep the hits sample-for-sample, and stretch only the decays between them. Separating noise and ring.
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@ButeSound Fair enough. I would posit though that messing with different algorithms applied to different material can also be a path to creative discoveries.
Infinite roads to infinite destinations really.
Introducing e-means, an algorithm beating k-means in 10x fewer epochs.
With order of magnitude wall-clock speedups, e-means is released in FAISS and needs no hyperparameter tuning beyond k. It is robust to various scales and datasets. Still skeptical ? Just try e-means [1/10]🧵
We had better AI music 40 years ago. Beyond the musical/style errors others have noted, this is only impressive if you don't know the field. A Markov chain trained on Bach does better. That was our LISP "hello world" when I studied with David Cope 20 years ago
It really does suck
it's embedded in a bigger system, alas no standalone thing to download. but claude can probs do it from "Cut the loop at every hit and keep each hit sample-for-sample, so nothing smears. Only the decays between hits get stretched, in two parts: the noisy part (snare rattle, hats, room) by overlapping short grains, and the ringing part (kick body, toms, bells) by a phase vocoder that picks up in phase with the hit before it. Slicing is what DAW beat modes do and the noise/ring split is from Driedger, Müller & Ewert (2014); the combination is what made the tails clean."
@alexanderchen Yeah, I love that you can now build some intuition without needing deep paper studies every time. Changes the opportunity cost economics.