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 allow for much better recommendations. I'm not sure what aspect you're using to order the results, but having extra metadata to filter or group the results in some way would help a lot.
Take Raga's Dance by Vanessa-Mae, A R Rahman, ... Royal Philharmonic https://maroofy.com/songs/476841571 . I put in this track expecting other fusion songs to pop up, and arguably some do, but much more often it feels like a 20 second section was used to define the original song and it misses the underlying concept. Like it got, in my subjective description, the epic violin in orchestral music, but it completely ignores the fusion between the distict styles of traditional indian singing/instrumentals and western ochestral and also ignores the call response structure between the violin and carnatic players, which is the what I actually care about. Other songs have the vocals but no epic backing. It feels like it's matching multiple samples from the song instead of the whole song.
This feels very promising since it clearly is picking up the styling of the specific songs across different genres and languages. I look forward to seeing where this goes.
I also think it would be interesting if there was a way to specify two different songs to find either only the common things and/or to find what the fusion of those two tracks produces.
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 conjecture type algorithm that eventually converges on the same tuned playlist for that genre, no matter what the starting parameters may be.
Ideally one would like an algorithm to be able to realize,"this person prefers to explore new music from the same era," vs "that person prefers to jump around to different countries," vs "the other person prefers to remix their existing playlists," and thus come up with the optimal degree of novelty for each listener. Or at least let the user set a novelty slider to customize their own experience.
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.
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.
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.
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
I'll note my own experience, that Spotify and Apple Music both struggle to find me latin reggaeton outside a small subset of popular artists, and my first couple searches with this tool have found me so much music I've never heard before that matches exactly the 'vibe' I want to hear, and is introducing me to different-but-related sounds and artists I couldn't have found on my own.
I agree with the other commenter - this is huge for me. Please, do whatever you need to do to monetize this so it never goes away. I would love to pay you for this.
I don't know if someone already said this, but as an amateur music producer i would love to upload my songs and discover similarities.
Thanks for this Amazing tool
Rather than get back anything "acoustically similar" it simply returned a list of other songs on the same album (several of which are far from being acoustically similar).
No drama, you're attempting to cover a lot of ground, but I'm guessing there was no actual fingerprint there for that work and no sense of other songs that sounded similar.
ADDENDUM: Okay, I had to select the song <doh> .. but still "something went wrong" - perhaps hugged to death or not found to process. No matter :-)
If I am not mistaken, it this is only trained on the preview and not on the entire song.
If you listen to a music with a real intro, it gives strange results. For example: "Goodbye Blue Sky - Pink Floyd" (https://maroofy.com/songs/1065976153)
IIRC they try to classify music on 17 different points/features. What you see on the web is an attenpt to visualise (and provide a guide to music based on) some of them
Yes. I think many of those features are based on pre-NN feature detectors (such as BPM), and Danceability, Valence and Energy sound like primary components that have been given names.
Echo nest was great for its time, but if they have kept up, they're not exposing their more modern learned features to users anymore.
They were acquired by Spotify, and there's been some work done by/for Spotify since then.
I'm not at liberty to say what, sadly, as I work for Spotify.
I think I can say that one of the main challenges is running this analysis for users. It's prohibitively expensive (or was prohibitively expensive) to use this to keep track of and run recommendations for what users are listening for each user.
It can be used on smaller scales, but, well, it's probably NDA :)
Can you say why Spotify's recommendations are so bad? Something like what OP has made should have been relatively simple to make for Spotify for many, many years already, yet that hasn't happen. Is the whole system just rigged to only recommended a few "sponspored" artists?
Because, as I said above, it's a very complex problem :)
I honestly don't know much about recommendations (and what I know I probably cannot tell). But there's definitely continuous work done on them. But it can also be hampered by extremely conflicting requirements (where "some" both means double-digit procent of users and these "some"s overlap with each other):
- some users want more of the same, some users want a more diverse listening experience. Some of these users are the same user, but on different days
- some users mostly prefer curated suggestions, some users want ranodm stuff. They can also be the same user :)
- some users a heavily weigted to only a few artists, some users listen to evereything and anything. And even this can be the same user :)
- there's probably stuff about licensing, availability, contracts etc. at play as well, because in streaming services it's always there, in very bizarre ways
Basically every single tweak to recommendations will break them. And yeah, Spotify employees will complain about this more than anyone else, all the time :)
I doubt that "why does your product suck" is one of the things a Spotify employee is allowed to talk freely about in public!
But I've been watching them, I will speculate. A few years ago, Spotify had two young interns, Sander Dieleman and Aäron van den Oord. We know a bit of what they worked on, because Dieleman blogged on it, and indeed it was something a lot like what OP has made here - only better, I would say. I asked him, and Dieleman was allowed to say that the thing they built was one of the inputs into the then-new Discover Weekly, which made headlines for how outrageously good it was.
But Dieleman and v.d.Oord did not stay at Spotify. They were headhunted by DeepMind, and have had a VERY impressive track record there over the years.
And I wonder why. Was there a conflict between the old school ML of the Echo Nest people and the new fancy neural net kids? Or was it just, as GP alludes to, that the NN methods were just too computationally expensive and they failed to justify their costs to leadership?
A distributed, local-first architecture much work well for this. I’m happy for my computer to crunch away on my behalf, generating recommendations and indexing stuff. I’m happy to recontribute that work to a common index of some kind.
I def prefer for that common index to have a permissive license though!
I had the same experience. Could see the element which it matched with, beat, pitch, etc.but missed the riff or nuance that made the source song special to me
I've only tried a few songs but they've mostly been bangers! I did come across a couple examples where the recommended songs just heavily sampled the original but overall very impressed.
Take Raga's Dance by Vanessa-Mae, A R Rahman, ... Royal Philharmonic https://maroofy.com/songs/476841571 . I put in this track expecting other fusion songs to pop up, and arguably some do, but much more often it feels like a 20 second section was used to define the original song and it misses the underlying concept. Like it got, in my subjective description, the epic violin in orchestral music, but it completely ignores the fusion between the distict styles of traditional indian singing/instrumentals and western ochestral and also ignores the call response structure between the violin and carnatic players, which is the what I actually care about. Other songs have the vocals but no epic backing. It feels like it's matching multiple samples from the song instead of the whole song.
This feels very promising since it clearly is picking up the styling of the specific songs across different genres and languages. I look forward to seeing where this goes.
I also think it would be interesting if there was a way to specify two different songs to find either only the common things and/or to find what the fusion of those two tracks produces.