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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
#92Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#93Earlier quoted context omitted.
The preview audio is free. It may just be 120,000,000 previews.
Totally explains this part of the current top comment here: > but much more often it feels like a 20 second section was used to define the original song and it misses the underlying concept
Yeah, like the linked The Medallion Calls example catches similarities to the slow starting section of the track and totally misses the main part
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#94Earlier 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 But the model architecture I'm using is kinda outdated as well, gotta iterate on it more to improve it further!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.
Honestly it's loads better than current Spotify/YouTube Music suggestions. Mostly they just seem to suggest popular stuff that's heavily marketed...even though I seeded all my "thumbs up" with only eclectic stuff. Yes, it's hard to find a song I really really like, but 1-in-10 seem to be something I'd add to my eclectic "thumbs up" playlist. And almost none of them are by any artist that I've heard of before. This is…
You're not alone. For me, Spotify suggestions are "things you won't hate." Most everything is palatable, but forgettable and too usually not all that interesting.
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#95Holy cow does this thing have wildly obscure taste in tunes. I plugged in “Work It” — Marie Davidson, and it returned “Beautiful Weather” — Blemow. This is an 8-minute opus of a techno jam from the album Dutch Cow #13—the 10th and final Holy Cow album released in 2018, a true annus mirabilis from Blemow.
IMO my idea for making something like this really cool is to give the user more explainability (why are these two songs similar? according to which factors?), and then more control over search results (brainstorming here, but stuff like an obscurity slider, importance of beat similarity slider, etc.). You can try to extract explainable factors from your embeddings with something like NMF.
(PS—I like the esoteric results. This is cool, good job.)
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#96This 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…
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.
Wanted to hop on and say this is amazing, thank you for sharing this! Also agree that it seems that it's really good at finding literally similar sounding songs, but not what I would expect a friend to recommend (this is both good and bad I guess). As someone else said, this is already way better than my spotify recs
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#97Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#98This works really well!
Could you add links to play songs on Spotify?
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#99[deleted]
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#100Earlier 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.
Honestly it's loads better than current Spotify/YouTube Music suggestions. Mostly they just seem to suggest popular stuff that's heavily marketed...even though I seeded all my "thumbs up" with only eclectic stuff. Yes, it's hard to find a song I really really like, but 1-in-10 seem to be something I'd add to my eclectic "thumbs up" playlist. And almost none of them are by any artist that I've heard of before. This is…
I never get any heavily marketed music recommended on Spotify. Almost invariably it's something obscure. But I only ever listen to obscure music. I guess I'm saying I don't think the Algo is weighted for payola.