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.

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.

Era? Artist? Genre? Sound? Tempo?

Personally I spend my time finding similar-era music because I like to hear how sounds evolved.

What makes it different from a big "play me a random song" button then?

> it seems to identify similar tempo and melodic riff

But the model architecture I'm using is kinda outdated as well, gotta iterate on it more to improve it further!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.

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 huge for me. Thanks.

If you need someone to test your model, you will never find one with more eclectic/strange taste than me ;)

1) https://maroofy.com/songs/1443834381

2) https://maroofy.com/songs/714207207

Nine Inch Nails

https://maroofy.com/songs/1440934933

Recommended Muppets

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.

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)

See this paper from https://everynoise.com/ : https://everynoise.com/EverynoiseIntro.pdf

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

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.

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 :)

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 :)

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?

I def prefer for that common index to have a permissive license though!

Examples:

The Oblio Joes - "Captain of the Moon"

The Bondage Fairies - "Levenus Supremus"

... both chosen so "Just shove a bunch of recent pop-rock at the user" won't work.

I've been using your site for 10 minutes and already added song after song to my library. I'm sure you'll improve the matching algorithm over time. This is a great first step; I'm being exposed to songs I've never come across before.

Good job!

I love that it's working by sound. So often eg Spotify will insist I check out other bands in the same "scene" or from the same era as other artists I like, and in genres as broad as "70s rock" it can be really tiring.

One of the first tracks I tried was Natural Woman by Carole King. I love that it recommended other slow but rhythmic piano vamps with tender vocals by artists singing in other languages, some modern, some old, as well as some sung by men. It even recommended me more Carole King which is I guess shows it's picking up on something constant in her music.

What impressed me was that it recommended quite a lot of numbers in the same key! It was funny clicking through them and the tonal centre being unchanged. It was like they really were different but the same.

It definitely doesn't understand everything about tonality though! I tried some atonal music next, Naama for solo harpsichord by Iannis Xenakis, expecting to get more atonality back. Nope, first result was very firmly tonal: "Suite in E flat major" by Bach for solo harpsichord. It definitely got some essential aspects of the pieces down but completely missed the central concepts underpinning their musicality.

Very promising like I say, can't wait to try more things out!

Probably my favorite song of all time is Close to the Edge, by Yes. Spotify will happily provide me with tons of recommendations for 70s prog - much of which I love too, but some of it leaves me cold.

Maroofy came up with "Good Day" by Leigh Ashford, a song I had never heard of. It's very interesting to compare it to CttE - it's not a very similar song in most respects, but has a similarly prominent bouncy bassline. I like the song, and I don't think I'd have found it via spotify or any of my other usual music discovery sources.

Overall this is very cool, great project.

Where Spotify's Discover Weekly tried to connect you to music other people listen to {B, C, D, ...} because you've listened to a particular song {A} {B...->A}, your model quite literally tried to find other music {A₂} that sounds like what you're looking for {A₁} {A₂->A₁}.

Edit: OK, some of these are actually pretty dope...

[1]: https://maroofy.com/songs/1458902217

What?

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

Your project is extremely motivational .. how long did it take you? What did you train on? I do DL for work and just play with things like cifar. This is so inspired.

120M songs would take approximately 1000 years to listen to in normal time and is way beyond normal usage.

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

Other than that, however, this seems neat. I've been trying to listen to new music lately, so this'll definitely come in handy.

There’s no way today’s Apple would allow such a collaboration. They’d just keep the feature and market it as part of Apple Music.

The top three responses were "La Raya" by Los Islenos [2], "Days of Our Love" by Deepa Dremata [3], and "Flying Home" by Michelle Mack [4].

While I didn't hate any of them, and they all featured a piano, I wouldn't say any of them sound like Keith Jarrett, either.

[1] https://music.apple.com/us/album/poinciana/1446740946?i=1446...

[2] https://music.apple.com/us/album/la-raya-feat-ben-murphy/154...

[3] https://music.apple.com/us/album/days-of-our-love/1608767255...

[4] https://music.apple.com/us/album/flying-home/1577716851?i=15...

it doesn't seem to understand anything about the style of the music

seems to find stuff which is sonically similar rather than musically similar, and even then I'm being generous

no useful recommendations

I have a human-powered recommendation service that uses my own tags that I've added to my mp3 library over 25 years. I add instruments (not all, just the ones that stands out, like synth, flute, distortion, violin, piano), vocals (male/female, falsetto, spoken, rap), moods (happy, sad, angry, mellow, dramatic, chillout) and genre (I don't go too deep here, because I hate getting recommendations stuck within some obscure sub-genre). And that's it. I get it to play a random highly rated track with a keyword or two, and then use the tags from the first 10 songs to generate the next. But since, for me, music is a somewhat interactive experience, every 10 songs or so, I'll think of something that I want on the list (maybe reminded of it by another one that just played).

Other things I think might be useful for recommendation is Last.FM histories. Think about it, the are hundreds of thousands of active listeners "scrobbling" their listening history. You could easily parse that and group songs together that have been played within 5 songs of each other as long as they're not by the same artist and the time between the songs is around zero (ie: listened to in order, no pauses). Similarity is higher for songs that were next to each other and score drops.

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.

For an extreme example of that, see the boorus. Some machine learning people have become interested in those, since they are huge dataset of extensively tagged material ... or maybe it's the booru people who have become more interested in machine learning. Either way, I'm sure they're great, if you're into waifu anime, porn, or waifu anime porn. Both types, country AND western, as they said in the Blues Brothers movie. Any tag remotely subjective (such as "beautiful", God help you) is going to be extremely coloured by the tastes of an extreme fringe.

At least, relying on fanatics to do the work for them, I assume they've got a handle on simple spam on the boorus. Commercial recommender service tagging systems don't have that luxury, and that's probably why they end up eventually removing them.

And don't allow free-text tags. Instead you give a list of available tags - the lowest number needed to describe most tracks. I mean, let people add their own if they want, but you should ignore those while training the model.

I actually think that instead of trying to tag some specific mood (eg "happy") some sounds be a sliding scale between two opposites:

happy<-------|--->sad

Instrument tags are easier to understand. Give a list of instruments (or instrument types, because the user might not know precisely which woodwind or percussion instrument it is) with checkboxes beside each.

Some users will be experts because they play woodwind. Let those users apply to become experts, pass a test, "identify the instrument", and if they pass, give them half price subscription as long as they moderate X tunes per week.

The later has generated more similar music for me so far. But I welcome every additional project improving the search for music which has been so neglected by most services.

This is so interesting out of the gate that criticisms seem rather foolish. But I’ll just say what I tried.

I tried pop songs, jazz tunes, and even rather strange, unique recordings like “One Step Beyond” by Madness. And every time I got something with similar aspects.

I also like that all cultures were represented.

Would I use this to find music? Maybe? It kind of underscores that we don’t always listen just for a particular sound, but for what that artist represents to us. I may really like a band and absolutely hate a band that’s pretty much a copy of them (maybe done a few decades later) precisely because they are too much a copy.

I don’t think I could build playlists (in this current offering) because the songs are _too_ similar. Maybe if I want an exercise playlist with a particular bpm and vibe?

I think if I were looking for music for TV or advertising this would be an absolute winner. If I feel like I want a song like Daft Punk here, but I can’t afford to license it, I can use your tool to find a lot of tracks with similar vibes that I can afford or that I haven’t heard of.

    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 succeed). Status code: (null).

    NetworkError when attempting to fetch resource  --  _app-f73fb5ecceb5fc1e.js:1:82295

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

//Edit: Just read the edit regarding ublock origin. So this website definitely needs a Privacy Policy then.

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.

EDIT: It's working on my desktop. Above was on mobile Firefox/Chrome.

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

> but much more often it feels like a 20 second section was used to define the original song and it misses the underlying concept

https://itunes.apple.com/us/lookup?id=1023678453 (https://www.chrisjmendez.com/2017/06/19/working-with-itunes-...)

So probably all that is required is a couple threads downloading and a proxy service with a large pool of IP addresses randomly rotating on every request. Maybe OP also found an undocumented API endpoint somewhere that was not rate limited.

https://music.apple.com/subscribe

Most other services try to find matches by seeing what other songs the people who like the searched song like, and finding trends amongst those. This site finds songs with similar sounds and rhythms by looking in the vector space. Awesome! Congrats on the finished site!

As others said, it would be a nice option to export it as a playlist for Spotify/Apple/Youtube or a .txt file with "Artist - Track name" in each line. Then you can import the txt file into a playlist converter tool like www.tunemymusic.com and play it in your favorite music service. Mine is discoverquickly.com where you can listen Spotify playlists fast with mouseover, and discover related songs/artists.

An autoplay x seconds of every track, where you can choose the number of seconds, would be a nice addition too. This way you can discover music while doing other things.

All the AI model seems to understand is the opening 5 seconds piano. Listen to the actual song it just opens like that because it's from a play.

I think this will struggle with any song that has build up, it's very promising, though you need a sample of entire songs not the sample Itunes gives you.

I actually think this is largely in part due to how the current model + training process is designed.

I have some ideas for improving things on this front, will give it a try and push an update soon! :)

Feels a little bit like fortune telling, I guess it is, in the sense that I am listening closely to what makes the songs similar, not just listening, but actively trying to find the similarities, so even a couple notes in progression, or drum-beats and I'll say oh, yes, that matches.

Finds very different music, not necessarily what I'd listen to in many cases, but kudos for getting me clicking through a decent pile before going wait, that's a nope, you're grasping AI-type-being.

A comment: for classical music, it would be necessary to see the name of the composer. Currently, it's only the performing artist that's displayed in the interface.

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.)

And a quick suggestion – all my top results when searching for popular songs were songs that were either different versions of it or those that had heavily sampled the original. While the algorithm is spot on, that isn't exactly what I am looking for. Maybe have a filter to exclude results that are too close a match?

[0]: https://www.gnoosic.com/

For example, any time I hear music I like in a Disney movie and want to hear similar music, all I can get recommendations for is movie soundtracks or whatever the guest artist is known for.

The example that covers up for me on occasion is the soundtrack to The Road To El Dorado, specifically the instrumental tracks (Cheldorado, The Brig, Wonders of the New World); Chosic.com gives me a ton of other soundtracks, mostly orchestral scores, for things like Kung Fu Panda, LOTR, Assassin's Creed, Angry Birds, POTC, etc. While it's nice to be able to tweak the algorithm on chosic to pick quiet movie scores for background music, that's not what I'm looking for. And gnoosic is entirely useless for that.

Seems like you are dodging the question of data access and I am not sure why. Even running an intelligent scraper to download 120M song previews sounds too complex and might take days to months as you have to rotate IPs and not bombard the server all the time. If you manage to do that, kudos. That itself is a great achievement. If not, can you let us know how did you get the data access? You might help other devs who want to try something similar

If not and he found an unauthorized source of retrieving information, this reveals a serious security breach in iTunes API and it’s my valid concern as a paid customer.

120M is a huge number and it’s not even text. It’s media

I can certainly see what the model is getting at each time, and I've not hated any of its suggestions so far, but I've also not stumbled over any new favourites yet. I don't know what kind of features the model is able to learn, I think it might miss one of the things I like most in music, which is not just dynamics, but something that builds tension over time and then blows up. If there are no longer range features like that I'd certainly experiment with them.

I've had a lot of success with Apple's own suggestions (which are admittedly extremely hit or miss), and I've probably grown my collection 1000% in my 30s and 40s after letting music drift away from me in my 20s. There's nothing better than the feeling of a new suggestion and you click through, and there's no artist profile because they're unknown, and they've got like 300 Twitter followers but you love them like a 15 year old. At least once on here I clicked through and found that I was listening to the only song ever recorded by someone, which seemed quite special.

It seems most forms of EDM work great with this setup! One funny thing is for heavily remixed tracks, all the remixes pop up as suggestions. :)

The current model does tend to do well with EDM, but got a new model in the works that should hopefully address a lot of the shortcomings of the current one!

I tried an obscure(?) Japanese rock band "9 Ball" and they were extremely high on the search results. I can't even find the album by using Apple Music.

Searching for 9 Ball in Maroofy shows them in spots 2-6, easily: https://maroofy.com/songs/185487468

When I try to search for the same band in Apple Music, I get this jumbled mess of results: https://music.apple.com/us/search?term=9%20ball

Even searching for a specific song of the band, the correct artist is 5th on the list: https://music.apple.com/us/search?term=9%20ball%2024%20hours and doesn't even link to any of their songs: https://music.apple.com/us/artist/9-ball/28599994

I literally cannot use Apple Music search to get to the page to buy Sound Seeds by 9 Ball, without going through Maroofy first. I had thought Apple removed that album years ago due to some kind of licensing issue. Strangley enough, even though there is an option to "Buy for $7.99" clicking on the button in Apple Music doesn't seem to initate any kind of prompt. Maybe this is old data, but the previews are still available? Who knows. Either way, the search for that information was excellent.

e.g. I'm sometimes seeing several essentially identical tracks at the top of recommendations (also mentioned in a comment by rayshan). You probably want to penalise tracks like that so they're pushed well down the list, i.e. penalise matches by metadata similarity (artist, title, etc).

OTOH I think it should boost results from more popular songs/artists, so the top result is less likely to be an obscure result that happens to sound similar. Some might argue it's a good thing to discover/highlight obscure artists, but for most users, it's more practical to recommend results that are already "proven" to be appealing. More obscure results could still be blended in if highlighting them is seen as a goal of the project.

I'd also add that it's usually a good idea to incorporate this setting into the URL, regardless of the UI, so that a specific order can be bookmarked and shared.

Even for those who are using a music platform, this project still has its unique algorithm that’s likely to surface distinct results. Partly because the developer can do their own innovation and partly because the platforms influence the algorithm in ways that aren’t necessarily aligned with users’ interests. e.g. promoting artists they have favorable relationships with.

The developer can also offer features that Apple et al aren’t offering, perhaps because it would complicate their app too much or isn’t high priority, but makes sense for a specialized tool like this. e.g. fine grained settings to filter and sort results.

Feature request: allow me to auth Spotify and click a button next to each track to add a track to a "Maroofy Recs" playlist

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.)

I was looking at Islands in the Stream by Dolly Parton and Kenny Rogers and it had some Latin neighbors that make some sense to me from the synths in IitS.

I picked a Three 6 Mafia song I used to listen to in my younger years, the particular song I chose has everything in the soundtrack from high hats, to trumpets, several rappers with varying styles of exerting their voices, and a lot of the resultant songs have either high hats or similar sounding trumpets, or similar sounding drums, and in other cases, something about how the artists rap reminds me slightly of Three 6 Mafia's own style. I don't recognize any of the artists suggested, which is a good thing, it basically did what it was supposed to.

Next I picked a song by Bullet For My Valentine, not all the songs are quite nu-metal but that's because the song I chose isn't typical nu-metal (or whatever genre they were...) unlike some other metal bands they dont spend the whole song screaming, they actually do normal singing, so it picked up what to me sound like old school metal bands like Dio, Iron Maiden and so on... So it picks up on how some parts of the song are, which can get you mixed results, so if you want to find a similar artist down to the musical style, pick the most "metal" song you can find by them. This isn't an issue for me since I like some of those artists too, but I would rather get more bands that are more like Bullet For My Valentine, though it seemingly did catch one or two I had not heard of before.

Also tried another metal band Killswitch Engage with the song "My Curse" and it recommended another Killswitch Engage song titled "For You" which honestly, is weirdly close in style and I never noticed that before. Again this one's another metal band, I just wanted to see how it would do.

Lastly, I tried a Spanish reggaeton song, and I was not disappointed. You really did a hell of a job on this.

My only wish is that Apple would hire you / buy out your efforts because I wish this was part of Apple Music, their "suggested music" is awful, and the genre discovery type of thing doesn't always yield artists I remotely care for, I feel more inclined to listen to some of the suggested songs but it sucks you can only give samples and link to the song directly, if you could generate playlists from the results it would be good, this isn't a you issue, its moreso an Apple limitation.

Some suggestions:

- Have a relevant/irrelevant button, so users can tag useful music and suggestions that were irrelevant. Save that data and use it to make your model better, either in realtime or incrementally.

- Allow some other options for sorting tracks also. Have the most relevant but also the most popular (as in charting) on top. Or maybe sort by the user rankings (how relevant they think a track is).

But of course I tried it with 2 very difficult songs I never could find anything like it: Desert Rose by Sting and Zoolook by Jarre. For Desert Rose it found some similarish things (but of course failed to capture the essence, what makes Desert Rose so unique) but for Zoolook it completely fell apart.

You could provide discovery services to these users in exchange for model updates and feedback. Couple thoughts on this:

- there are modern techniques to update an ML model at many edge locations, then combine the learnings without violating user privacy. One common application is type-ahead models.

- People who have large local music collections tend to care about music, and would take the time to provide high quality labels for you.

- computers used as media servers often have unused compute cycles because music playback is not that intense and most folks don’t have music on 24/7. You could harness these to reduce training costs for your model

- These libraries would give you access to the long tail of the music catalog, including many things that aren’t on iTunes or other streaming services

- This would also put you in a position to run an open music catalog. Your embedding index would be a key differentiator from existing options.

1. Please add match score

2. Group and fold duplicates

3. Add the year with the sort feature - to identify rip offs

But for now, ended up using plain old FAISS.

My only request is to please let me multiselect some or all of the songs the algorithm finds and automatically create a playlist from them on Apple Music. The very first search I tried, for a song that Spotify always creates the same limited mix from, brought up a ton of music I want to check out.

Edit: on that note, it would also be neat to request a combined playlist that mixes together multiple searches. This might help provide feedback for the AI as well about what artists/songs people consider 'similar' to each other.

Say, to figure out what are The Pixies ('measured') musical references (y)

For instance I picked a song with a very strong snare drum line. All the suggestions also had a strong snare drum line but wildly different melodies, genre's, tone, etc.

Got some stuff in the works for an improved model, hopefully will be able to ship it soon!

1: https://www.youtube.com/watch?v=Eks6KcV2ufg

2: https://www.youtube.com/watch?v=IYPfbX0DmrE

Love your application! Great job. Found some very surprising similarities to many songs that I tested.

Here's the link: https://maroofy.com/songs/1159778217

Really cool project.

But a lot of this should go away with a better, improved model!

If you ever write a blog post about the process of making this, I'd love to read it.

To me, it's fascinating that not only can you:

-represent things like words as vectors,

-map them in a multi-dimensional space, and

-use that space to find the "closest" neighbors (i.e. the most similar words)…

…but you can actually perform "mathematical" operations on them.

The canonical example is that, if you represent "king", "queen", "man", and "woman" as vectors in your embedding space, then you can ask your model "What is king - man + woman?" and (provided it's trained appropriately) it will return "queen".

I look forward to the day when we can ask something like "What is 'Bohemian Rhapsody' - 'Queen' + 'Velvet Underground'?". Which, if OP's model were to be trained on whole songs instead of previews, would probably be a reality!

Also this song, Shangri-La Is Calling, is bugging out: https://maroofy.com/songs/1632142336

The similar songs for Anaal Nathrakh - Endarkenment [0] have Annal Nathrakh with エンダーケンメント at the top. So a misspelling of the band, and apparently (according to google translate) a Japanese transliteration of the song title (Endākenmento, enderkenment). The song itself is literally the same one, others listed are their other songs, or more Japanese transliterations of their songs. The weird versions can be played, but the linked page does not exist on Apple Music [1]

[0]: https://maroofy.com/songs/1522388463

[1]: https://music.apple.com/album/%E3%82%A8%E3%83%B3%E3%83%80%E3...

https://maroofy.com/songs/1065976170

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?

This happened to me before, I'd point out that for example Reckoning Song and Counting Stars are the same song, but people would swear they have nothing in common. Or Sail vs Believer, why was the second one even made. Is it that people focus mostly on the manner/vibe of a song, and don't notice what it actually sounds like?

Because I tried another song: "Sam Sweeney - The King of Prussia's March", and the second recommendation was "Polsk Nr. 48" by Rasmus Storm.

The wild thing? Well, first of all, Rasmus Storm isn't strictly speaking the artist. He was the composer. He was a 17th century Danish fiddler, who - rarely for his time and social standing - knew music notation, and wrote down his tunes in a book.

The crazy thing is that in that same book, "Murchy nr. 14" is the tune better known in England as "The King of Prussia's March"!

The odds of that happening by chance has to be absolutely tiny. "Obscure" doesn't even begin to describe it when it comes to Rasmus Storm's notebook.

Still really excited to see updates on this, especially recommendations based on a playlist, since the big 2 (Apple Music, Spotify) haven't figured out recommendation for a all-over-the-place music taste yet.

I wonder if this app has more potential for the record companies and songwriters in terms of finding copyright infringement than it does for the consumer finding new music they like?

Currently, the website seems down though?

EDIT: the results are ... not what I expected. They are similar in some sense, but I wouldn't consider them "musically" similar. It's like each of the recommendations has something similar, whether the drums, the baseline or the voice. But none of them feel similar in the whole.

It's as if similarity is measured with an L2-norm in a high-dimensional embedding space, instead of cosine similarity? Did you experiment with different scoring metrics in semantic search? I would recommend cosine similarity, if this is an embedding space trained with neural networks, whether with a contrastive loss or with a gaussian prior.

This would also explain why people love and cherish songs that would objectively speaking be considered bad.

The feelings that people feel when they listen to the song, the environment that surrounds us and the situations that we're in.

But it is an interesting discovery tool!

Is this AI or machine learning?

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).

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.

Probe 1: Birdland. First hit was the canonical Weather Report recording. 2nd or third was a popular Man Tran version. Then I saw a Maynard Ferguson track — ok that was a discovery as I haven’t listened to Maynard much for a few years. Didn’t like his version much, but still your software gets full points for discovery.

Probe 2: Minor Swing. First hit was obscure, and it linked off to a bunch of totally unrelated stuff. Django nowhere to be found.

One awesome use-case I can see for this though is finding alternatives to copyrighted songs. Let's say you make a sports video, and you have this fantastic song in your head, but you can't secure permission. It would be cool if this could find something similar to that song. Same style, tempo, etc. Even better if it's royalty free.

Does anything similar exist that lists possible genres for a given track?

I’m a big fan of things like Ishkur’s Guide to Electronic Music and everynoise.com. It would be cool to be able to list out the genre(s) and show similar tracks.

I used Chrome. Cheers!

Edit: also, clicking away from the text input field shouldn't clear the value, in my opinion. The element which displays the results can still be hidden though. It would also be nice to control the volume of the played song.

https://www.plex.tv/blog/super-sonic-get-closer-to-your-musi...

It's true though, there is nothing else quite like Crimson and Clover. I'm pretty sure when I was 5 years old and first heard that song it caused me to trip. Strawberry Fields Forever is like that too, you get high just listening to it. ;-)

Will definitely work to improve the current model!

Still, this is a really cool project and I'm sure there's a lot of potential for building stuff with it.

It ends up recommending piano songs, many Korean ones.

There's some interesting ones like Ghost - Cirice, where it finds other songs with similar riffs. I like Ghost's music in general, just not the Satanic themes, so this is a great tool for finding similar music.

I'm somewhat amused that it doesn't match Under Pressure with Ice Ice Baby.

What would be a good NN architecture for the first step?

We'll likely see a lot of first generation products tackle these low hanging fruit like "I dumped data into AI/ML and here's the output". But the next generation will likely be people who can handle data better - interpret it, clean out bad samples, and so on.

When I was a teenager, I thought no band was as cool as Rainer Maria.

Does this mean I was right?

:D

Nice app! I tried it for another fave https://maroofy.com/songs/1578598760 and will give those a listen.

Curious what sort of metrics are taken into account, and if any supervision is provided.

https://maroofy.com/songs/1485025897

The one of "similar-sounding" is:

"Смели Граничари Димитър Коларов & Стоянка Колева"

or

"The Brave Border Guards"

It's a very Soviet song from such a different world that if you send "Man Man" to it in a time machine, the group will immediately end up in the Gulag.

So, it's all about the "similarity" definition. But it's fun of course.

It kind of matches genre, but has no grasp of musicianship or why I might like a track.

And this is a fundamental problem. See Avery Pennarun's brilliant explanation of tracking users, data analysis, and recommendation systems to understand why:

https://apenwarr.ca/log/?m=201902

Also, other comments have suggested that this was trained on 120MM of the audio previews instead of the full songs. That might explain why the recommendations seem a little off for some people.

I tried two of my all-time favorite songs:

The Gaslight Anthem - Handwritten

Thrice - The Artist in the Ambulance

On the first, I would say it was a total swing and miss - the recommendations had similar elements to Handwritten like strummed power chords, but the vibe was completely off. With Thrice, same thing. The first match was a remaster of the song itself by the band, so of course despite being a "different" track it was the closest possible match. The rest of the recommendations had a similar tempo and more heavy metal riffs (as are common in Thrice's songs), but none of them were songs I would voluntarily listen to.

This is a cool idea and I hope OP if you read this you take the criticsm as it's intended - I'm not trashing the implementation or anything. This just feels like it truly is a robot that can't feel the emotion of a song making me recommendations.

The current model I have isn't as good as I want it to be, and I'm working on a newer one with a different training process as well. This should address some of the shortcomings people have mentioned.

I was really nervous of shipping my v1 with the current model, but thought I might as well share what I have so far with the world, in case someone finds it useful lol.