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Show HN: I trained an AI model on 120M+ songs from iTunes

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Re: Show HN: I trained an AI model on 120M+ songs from iTunes

#251

Post by subtech »

bombela wrote: post #190

Everything loads instantly. Plays almost instantly. And it really seems to find very similar style/beat/music. Interface is clean. I am not sure if intentional, but the loading animation on the play buttons feels like it is in sync with when the music starts playing. Makes for a responsive feedback.

Thanks! :)

Let's just say that setting up the backend systems involved a lot of tears & frustration lmfao

Re: Show HN: I trained an AI model on 120M+ songs from iTunes

#252

Post by subtech »

jameshart wrote: post #12
goldemerald wrote: post #6

Wow, these songs were impressively similar to my queries. I would love for a interpolation playlist feature, where I put in 2 different songs (from say, classic rock and EDM), and I could get 10-20 songs that slowly change from the start song to the end song.

Yes, this: can you get any interesting insights by playing with the embedding vectors? What happens if you add embeddings together? Weighted average of multiple tracks? Follow the average vector for an artist's work over time?

100% This is definitely worth exploring, and I'm currently trying to figure out the appropriate front-end UI/UX to expose this functionality for users.

Re: Show HN: I trained an AI model on 120M+ songs from iTunes

#253

Post by vintermann »

cousin_it wrote: post #239
vintermann wrote: post #217

I entered "Spiro - The Vapourer", a brilliant dance music inspired instrumental folk piece with intense, layered melodies. It recommended "Buzz Cazon - Sentimental Attitude". I have trouble putting into words how bad that recommendation was. Seriously, just compare those two pieces, do they have ANYTHING in common besides maybe vaguely the tempo?

Wait, you really don't hear the similarity? To me the two pieces sound uncanny close. The tempo sure, but also the rhythm guitar does almost the same thing, and the higher pitched instruments in both have similar timbre and do similar things. You could probably mix the two pieces together and it'd sound alright. This happened to me before, I'd point out that for example Reckoning Song and Counting Stars are the same…

I think maybe you can argue that from the 30 second excerpt from the middle of the song (which I guess the model is trained on). The Vapourer briefly drops into the "Arches" theme there, an original tune from one of Spiro's earlier albums which is one of the overlapping tunes in the piece. But I still don't think the similarity is great!

Re: Show HN: I trained an AI model on 120M+ songs from iTunes

#254

Post by subtech »

I just wanted to quickly THANK EVERYONE for taking the time to check this project out and give your feedback!

I honestly didn't expect this project to get this much traffic -- I really can't express my emotions via text rn lol.

I'm working on an improved AI model that should address a lot of the shortcomings of the current one, along with a lot of other features people have mentioned (playlists, deduplicating results, volume control, better search UX, etc.).

Got a lot of updates coming, time to ship! :D

Re: Show HN: I trained an AI model on 120M+ songs from iTunes

#255

Post by japanman425 »

wayeq wrote: post #156
japanman425 wrote: post #116

Earlier quoted context omitted.

… what?

{W₁} {H₂->A₁}*T?

My thoughts exactly.

r/iamverysmart vibes… someone who has watched one Lex whatever his name was video on the maths of neural networks…

Re: Show HN: I trained an AI model on 120M+ songs from iTunes

#258

Post by raffraffraff »

Seems to work well on some stuff, not on others. I get that AI is able to correlate things that we can't really detect because we don't experience music as a raw waveform. But the emotion and sentiment in the vocals regardless of music genre is something that can link sings together in a playlist, and I don't see how this could do the same unless it's also considering mood tags (if you can get access to decent quality tags) and lyrics.

Re: Show HN: I trained an AI model on 120M+ songs from iTunes

#259

Post by amatecha »

Super sick, thanks for sharing this. I found some really interesting songs and artists to check out later. It's very time-consuming to listen through all the "matches" as many of them are not close to what made the "source" track appealing to me, but that's difficult to quantify for each song. Still, tons of stuff bookmarked and added to my Bandcamp wishlist :)

Re: Show HN: I trained an AI model on 120M+ songs from iTunes

#260

Post by twalichiewicz »

Nice work on this! As people have commented already, seems to provide more "very similar sounding sounds" versus recommendations.

I tried Glass Animals - Heat Waves, and after clicking through the recommended songs it's eerie how similar they sound to each other. As I would let the preview play and start the next song I sometimes thought my click didn't register because of just how similar one song would be to the last (https://maroofy.com/songs/1508562516 for those who want to see for themselves).

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