Suno Trick for Musicians: "Producer's Ear" tl;dr: - upload smth you made - use v5 + set to "cover" - match lyrics + leave styles empty - 0% weirdness, 0% style influence, 100% audio influence ===> Suno will "mentally" reconstruct your song = share "how it sounds to Suno" --- What is Suno? Probably an extension on Meta's MusicGen, the details are complicated but the key is that it's trained to reconstruct the input signal, same deal as "next token prediction" in LLMs (reconstruct the next token), goal to minimize loss = difference between input and output. So the network learns how to represent the signal so that input ~= output. They key is that the Suno model has some implicit representation of music it learned by listening + reconstructing a LOT of examples. Most of what Suno-the-company "wants" you to do is novel generative tasks, like with ChatGPT etc, but why not leverage the model for what it was actually trained on: reconstruction? When you feed in something you made, Suno acts as a bottleneck where its latent representations of your input "light up" and then it outputs a track that's close to yours based on what it "understood" about your track. Practically speaking, Suno is *telling you how it "heard" your track*, by sharing the version that it reconstructed "in its mind". Suno's reconstruction is probably similar to how "reconstructive" or critical listening works in general: you hear the music, understand in terms of some learned patterns (your knowledge), and then, if you were a real producer, offer advice based on this knowledge. Of course, the band can and often will throw out the producer's advice, but it's interesting to learn how your music is understood by an external party, what elements are preserved and what get modified. Nudge up weirdness slightly and Suno will start "hearing" elaborations on your track. Or nudge up style influence + add some small style cues and you can start moving the reconstruction in different directions. In general it's a useful tool for hearing possibilities + where you land in terms of a generic listener's internal audio representation space. (Q: Why not use the built-in style tagger? A: It's probably a different, much smaller model running a CLAP-style captioning. Actually going through the reconstruction will give you a lot more detail.) (Also note that based on a few comparisons I did of reconstruction with v4.5+ vs v5... the "big model smell" on v5 is obvious. The problem with v5 is that the text conditioning layer is very difficult to work with, whereas v4.5+ seems to handle style tags better -- @suno pls improve)