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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

#361

Post by criddell »

zuminator wrote: post #153
knaik94 wrote: post #24

This is very interesting, but unfortunately I haven't had the greatest luck in finding new songs I would enjoy listening to. It absolutely finds similar sounding tracks, but it doesn't distinguish which part of the song made it enjoyable. There's no tempo consistency or genre consistency or even main instrument/vocal timbre consistency between recommendations. I think locking one or more of those dimensions would all…

I agree with everyone's criticisms that it seems to identify similar tempo and melodic riff, irrespective of genre. But to me this is a feature, not a bug. I could see this or something like it opening my eyes to music I would never possibly have found on my own. I really like it! Spotify on the other hand seems to want to send me to the same group of artists and tracks I've listened to before, following some Collatz…

Spotify wants you to listen to the tracks that they are paid to promote.

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

#362

Post by jrd259 »

It did well on ""Eyes of the World" Grateful Dead finding some artists with similar vibe I never heard from. It did poorly in "Heart of the Sunrise" by Yes and "Nautilus" by COVET https://maroofy.com/songs/1084458728.

One thing that stuck out for me is that for some genres (not jazz, not classic) the singing really matters. Covet, for example, is instrumental music, no singer at all. I wonder if you'd get better results by separately training on the vocals and the rest. (I think I've read that vocal extraction works these days, though I confess it's a lot more work.)

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

#364

Post by alastairp »

raffraffraff wrote: post #204

I'm wondering if anyone has done something similar, but instead of trying to find similarities in the raw audio, they use tags available from sources like Last.FM, Musicbrainz, Discogs etc? And the ultimate answer to that is probably "those sources kinda suck". Discogs is like a trainspotter on the spectrum, fascinated by release IDs. Musicbrainz is kinda similar (each song will have a dozen matches of wildly differe…

In fact, ListenBrainz (partner project to MusicBrainz) is doing some stuff similar to what you mention about listening histories. We're using the data to generate similarity based on when songs are listened to each other in "listening sessions" along with other songs.

Follow the troi-bot user with a ListenBrainz account, and we'll generate you a daily playlist: https://listenbrainz.org/user/troi-bot

This is still very much work-in-progress, but we're doing as much as possible out in the open to solicit feedback from people.

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

#365

Post by alastairp »

macrolime wrote: post #352
raffraffraff wrote: post #204

I'm wondering if anyone has done something similar, but instead of trying to find similarities in the raw audio, they use tags available from sources like Last.FM, Musicbrainz, Discogs etc? And the ultimate answer to that is probably "those sources kinda suck". Discogs is like a trainspotter on the spectrum, fascinated by release IDs. Musicbrainz is kinda similar (each song will have a dozen matches of wildly differe…

I've tried with Discogs and found it to work pretty well. Kinda similar to what OP did just the "embedding" vectors was created by the Genre/Styles on Discogs. I didn't have a Vector database though, so it was kinda very slow. On Discogs those tags are per album and not per track. To create a playlist of say 10 songs similar to a song, I'd find the ten closest albums, then search for them on last.fm and pick the most…

A similar embeddings model based on Discogs genre/style data is the Effnet-Discogs model made at the Music Technology Group at Universitat Pompeu Fabra: https://replicate.com/mtg/effnet-discogs

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

#366

Post by sogen »

anagram666 wrote: post #155
subtech wrote: post #33

Earlier quoted context omitted.

Hey thanks for the feedback! I definitely have a lot of improvement to do on the model, it currently performs better for some styles/genres of music than others. But the model architecture I'm using is kinda outdated as well, gotta iterate on it more to improve it further! I'm also thinking of letting users upvote/downvote results, which can also help improve quality on the ranking side.

Another strange music for your testing that gives complete bonkers recommendations: https://maroofy.com/songs/1486467186 If you need someone to test your model, you will never find one with more eclectic/strange taste than me ;)

Another bonkers one:

Nine Inch Nails

https://maroofy.com/songs/1440934933

Recommended Muppets

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

#367

Post by stephc_int13 »

I tried it with a few tracks I really like, but no luck with the results.

They had some similarities in the tonal range, but not in style or quality and were sometimes completely different musical genre.

I can see some value in this kind of recommendation system, but this is a lot of work and it should probably be flexible enough to learn from the user, not with a single track but a complete collection.

I also think that automatic playlist management is rarely well done.

In practice I am often disappointed by simple shuffle algorithms, the critical part, again, is that we all have different taste a good software should be able to somewhat match those.

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

#368

Post by [deleted] »

yellow_lead wrote: post #288
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.

Hm, maybe it's the hug of death but I can't search for any songs as of now. (Stuck loading) EDIT: It's working on my desktop. Above was on mobile Firefox/Chrome.

[deleted]

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

#369

Post by SketchySeaBeast »

huhtenberg wrote: post #306

Search doesn't work in a recent Firefox on Windows - typing anything in the search box shows the "Loading..." dropout below it and then nothing happens. This generates no network activity and the console shows: Cross-Origin Request Blocked: The Same Origin Policy disallows reading the remote resource at https://cdn.segment.com/v1/projects/F4GFNelOpRsgUJc6iwTuiXr2t6AH5LCY/settings. (Reason: CORS request did not succee…

I've tried in both Firefox and Chrome and getting the same issue. I assume settings holds that magic that makes the network requests because right now none are being made.

Edit: Turned off privacy badger and then it started making requests on Firefox.

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