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

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've never commented on HN before, but I feel compelled. Congrats, you get my first.

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!

This is actually great, one of the most promising recommendation algorithms I've come across.

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!

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.

Tried Erreur 404 by L'Impératrice[1], and I noticed the beat of the other recommended songs were eerily similar!! I'd argue your project is actually too good.

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

How did you get the samples for the song? iTunes allows scraping?

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.

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

Finding sources for input data is something I struggle with when building deep learning models. Out of curiosity, how did you go about programmatically accessing the music files for all 120M+ songs, in order to create your embedding vector? I can't imagine iTunes has an API which would let a person do that.

I searched for "Poinciana" by Keith Jarrett[1] (one of my all-time favorites).

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...

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 different quality). Last.FM tags are used-generated which make some of them amazingly useful, and others amazingly detrimental.

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.

Quite impressive given you’re working with tiny snippets of songs. Kudos.

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.

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 succeed). Status code: (null).

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

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.

How did you get access to 120,000,000 songs from iTunes? Not just the listing, but the actual audio.

This is so sweet. It's finding gems that when I look them up on YouTube, are 10+ years old and sub-200 views. Total sleepers, but I can discover them through this service. I love it. Great work.

How did you scrape the audio of 120M songs? That sounds expensive?

How the hell do you even get access to the entire iTunes catalog?

That is a great idea! Measuring distance between embeddings has always been a cool concept (ex. If I have a vector that represents the word "king" and from it subtract the vector that represents "man" then add the vector that represents "woman" it will approximately equal the vector for "queen") and it's awesome to see the same concept applied to music.

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!

Fantastic tool, thanks for making it!

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.

Possibly an unpopular opinion (which is ironic), but I think this could do with taking popularity into account. The vast majority of music out there is generic landfill at best, and just outright bad at worst. I think once you've done the first pass and got the sonically similar songs there should be some kind of filter which prioritises tracks with higher listener counts which would inject some 'wisdom of crowds' into the decision making. It would be a pretty cool feature to then be able to have this setting on a slider with obscure at one end and popular at the other.

I've found probably the most egregious example of what knaik94's comment is talking about: https://maroofy.com/songs/214977681 ("Being Alive" from Company)

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.

Neat, but I'm convinced after giving it a try that this is absolutely not the way to do music recommendation. The recommendations were complete misses for me.

Tried with a song I knew well- Everything In Its Right Place.

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.

Well done!

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.

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.)

Solid work, but this only reinforces my belief that music recommendations is an area that is not (yet) cracked by AI. You can find similar songs with great accuracy, sure, but even if two songs are close to each other by all calculable metrics they can still vary drastically in subjective quality.

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?

Tried few recommendations and can definitely use an alternative for Spotify radio.

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

How did you download that many songs? Wouldn't that be something on the order of 360 terabytes?

Nice snappy UI. I found a couple of times on iOS that after I'd clicked through to Apple Music, if I returned, the app had somehow stopped being able to play again but this is very well done. I think I would give a boost to song names over bands/albums in the search as I think a fuzzy song match is probably more likely what a user wants than an exact album/band match on something they've not heard of. I'd definitely use a playlist feature that just queues up the top 20 matches in Apple Music, but I don't know what the API looks like. Anyway, only feeding back because it's great.

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.

This is FANTASTIC! There's a ton of naysayers here, but I'm going through songs and having a great time with this.

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 search feature is great from what I can tell!

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.

Something I’ve dreamed about but haven’t found: a tool/service/etc. that can take my tastes from an era, say my eclectic early 2000s mix of jazz, electronic, and tango, and find me a similar set of music I might be interested in from the 2020s or the 1990s. I would love to explore my own taste in music in different eras. Interesting work. Reminded me of my little dream.

Exciting to see AI used this way. My main feedback is I'd look at incorporating other factors to rank results, not purely how similar it sounds. Audiophiles might prefer a pure similarity ranking, but that could be offered as a non-default setting if anything.

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.

https://maroofy.com/songs/724345280 That's just the same song always 6 times in a row. you probably already know, one would prefer to have removed versions of the same song by the same artist at the very least.

Bro, don't listen to the critics. It is amazing as it is! Don't fix it if it's not broken. This is much better than my spotify recs. Much much better!

This is a very music recommendation engine. The best feature I guess would be the serendipitous finding of unknown artists we might not hear otherwise. Great job!

This is a project I've always dreamed of building! Already pretty excited about some of the recommendations I'm getting in some niche genres.

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

I'm sure this has a lot of value, and it's cool to see. I immediately am interested! One thing came up in the first search that's frequently overlooked in music, though: unless the prompt is Christmas music, the response should probably not be Christmas music.

Holy 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.)

Feedback: "sounds like" for music is more than just rhythm and tone. The search results are all useless for me because two songs with similar sheet music often have wildly different lyrical styles and subjects.

Nice and very responsive, well done. I searched for "Imeprial March" (the John Williams Wiener Philharmoniker version) and this was one of the highest ranked results: Inauguración del Ferrocarril (by Nathan Stornetta). I kind of chuckle thinking about what a happy empire this would be with that theme song. And of course it also answers my initial research question...which national anthem is highest ranked for Imperial March. The answer is Taiwan...make of this what you will. Star Spangled Banner is the second one a bit down the results list.

Interesting, I tried a few songs and artist combinations:

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.

Good work! I'm trying this out. It seems it tries somehow to match keys tempos and percussion characteristics.

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).

The similarities between songs are quite literal (e.g. songs are similar in some technical sense), but their mood, cultural meaning, etc., is completely different. I got some pretty festive pop as recommendations for similar songs to Enjoy the Silence from Depeche Mode - but the chord progressions and amount of syllables in choirs were similar. No idea how this works and whether that's by chance or on purpose.

It's a great idea and I like it a lot. Especially the execution. It loads nearly instantly and has everything I want there.

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.

There might be an interesting open source / self hosting angle to this. Some folks have a large library of music stored locally. Platforms like Roon can give you recommendations on top of this, but are expensive and include a lot of other features.

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.

Interesting. Some feedback

1. Please add match score

2. Group and fold duplicates

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

Nice work. Are you going to write a blog post on the process. Would love to read it.

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 :)

What vector database do you use? Did you run into any scalability challenges there?

This is incredible! I've gotten really into some niche genres this year and in trying to discover more of it I have been really frustrated by Spotify's limited radio mixes - it seems like I've been "black holed" into the same 100 or so songs with rare exceptions, for any of the albums I autoplay from in a given genre. This really might be the best music discovery tool I've ever used, it reminds me of how magic Pandora felt the first time I used it.

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.

- How about an aggregate function and year/decade result filters?

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

Interesting, I put in a bunch of my favourite songs and found nothing I liked. The songs seem to be over matching on drum beat and tempo so there is a similarity to the song I suggested and the matches but it's often superficial.

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.

Possible bug: I keep getting an error when I try to find similar songs to any song by Neutral Milk Hotel. (For example: O Comely, King of Carrot Flowers Part I) https://maroofy.com/songs/5611590

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

Wow great project. I tried it with a song and I kind of get what I expected. The results are more unique compared to other song search sites

I've spun up Hitchin' a Ride by Green Day and I think the results are quite interesting. Despite them being completely different from one another, they somehow manage to catch the vibe of certain sections of the song.

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

Really cool project.

It's actually great. I got a ton of good recommendations. The key is to actually use it to search songs of similar beat or tone.

This is amazing. I actually really like that it gives you songs across completely different genres and moods (while keeping the beat). One piece of feedback - it'd be nice if the search box didn't clear if you click out of it to e.g. look at another tab while it loads.

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

It's quite good. Tried a couple house/electro/ebm tracks and it gave a good mix of stuff you'd expect and novel stuff you wouldn't. It finds a lot of songs that sample a given song, for obvious reasons. Glad someone gave this a go without any collaborative filtering or user ratings, just digging through waveforms alone.

I'm teaching myself deep learning at the moment, and learning about embedding vectors was the first "holy shit!" moment I had.

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!

Interesting idea. I think it needs some finetuning to find songs that are similar but not covers. For well-covered songs like Bohemian Rhapsody, this is more like a cover-finder.

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

I think I either found some kind of bug or Apple Music is really weird.

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...

Can you do some deduping? A lot of the matches are just the same song appearing on multiple albums or single vs. album version.

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?

If you could somehow filter the beginner bedroom producers the results might be even more compelling...but great job so far!

Super interesting to read the varying opinions on here. Seems it differs a lot for music styles and personal preferences. I tried a few songs from my favorites and while the similarities were cool to see, it didn't correlate to actually liking the song for me that well. Then again I couldn't say what makes me like a song. My best guess currently is a correlation the voice of the singer(s) for songs with lyrics but not the text in itself.

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.

Wow. The songs it suggests have such a similar tempo and vibe that when I try it with songs I don’t know I can’t really remember which is which.

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?

"similar-sounding music" is indeed a good way to put it. 120 millions ! This is awesome! Reminds me of https://cyanite.ai/ (Search by audio). I will have to give it more time, but so far, I like your results better. Well done!

This site works great for EDM. For example, just lookup some random future house song and it will pump out tons of similar tracks. This is a great tool for people that want to keep music consistent in their content, but don't want it to get boring. I will be using this for as long as possible!

Lot me try this with Lotuk by Arsenal. Every other service will only recommend other songs from Belgium, disregarding any other form of similarity (to my dispair).

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.

The results do sound very similar, which is interesting from a philosophical point of view: after searching all my favorite songs, I liked not one similar sounding result. What makes one enjoy art vs. not? There's clearly more to it than how something "sounds."

A good chance OP is going to hear from Apple legal, Apple recruiting, or both =) But cool project!

I think what you've done is very cool! But I haven't had much luck with it, I think it's because of the emotions that I've anchored into the songs that I like, which is very noticeably missing in the songs that are suggested.

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!

What part of AI is this? Is it something related to how Shazam worked, as per their White paper. The idea is to create a visual image of the song, it's representation and run a model to identify things similar to it.

Is this AI or machine learning?

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).

This is great! It works quite well. I would love to see something like this with the ability to use license the music for a Youtube video so that you could get good music for a video, and exposure and money to lesser known artists.

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.

Didn’t do much for me. But I am an atypical listener — most of my playlists are different recordings of the same jazz standard by different artists or just same artist different era.

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.

Personally for me the best way of finding new music is friends whose opinions I trust. I find going by similarity will pull up a lot of derivative artists that suck (eg Last.Fm/Pandora). I want something good, and if it's radically different, even better!

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.

This looks great.

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.

Seems like your server is down or overloaded

Other people have called out limitations and I don't disagree, but I'll say that I do find the different approach, based entirely on sounding similar, interesting. Recommendation engines in music streaming services often tell me about related things I already know I like. That's not necessarily unwelcome (if I'm listening to one song that appears in Footloose, sure, it's not a bad surmise that I might like to hear another), but this tool surfaces a bunch of stuff I would have never thought to look for or found normally.

Good work! I tried some house tunes. I guess the results were similar but expected something more direct.. a suggestion; since some listens only includes the intro - it would be a good idea to match the first 5-10 seconds in harmonics; probably in the span of 90-180 hz. But I guess that would take a whole lot of new work.. So, anyways - if there's a way to jump in a bit into the song I think it might be beneficial. But after trying a bit more I realized simply that the quality of good productions was more of an issue. Great work anyways!

Hi! The website looks awesome! I have a problem with searching though. I can see the recommended songs below the search bar, but as I type to search something, it is just loading infinitely. I checked the network tab in DevTools and there is no requests made when typing or after typing and pressing enter.

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.

This is cool. Have you tried Plex? They offer sonic similarity matching that's trained on a user's personal music library. I think it might be fun to do a comparison? This feature was added in 2021 though so I'm sure models have improved since then.

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

I tried with my favorite song and uuuh... https://maroofy.com/songs/1099847184 Most songs don't sound like Minerva at all. :(

There is nothing like "Crimson and Clover"? https://maroofy.com/songs/59412471

You are into something here I am telling you, while spotify does provide recommendations of what you would like to hear, it does not provide similar sounding songs, as a musician I love it.

I wonder why it doesn't find Weird Al songs. If you search for Michael Jackson's "Bad", I'd expect to see Weird Al's "Fat", but neither one appears in the other's results. Instead, the actual results are interesting, because you can hear some similarities if you listen closely. But they're not what I'd consider "similar"

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

It seems to be finding snippets of songs similar to snippets. I tried on Metallica - Unforgiven III, which starts off with a slow piano composition, and then enters a riff and cuts out.

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.

Really great tool. I found a lot of similar songs for less well known songs of mine where Spotify and Youtube Music both have stopped delivering any relevant recommendations!

Lots of duplicate tracks! Is that costing you more to train? Like, if I type a song I find the same recording on the original album, single, greatest hits and a compilation album.

https://maroofy.com/songs/1651341589 is one of my favorite songs, but it says, "Hmm, something went wrong," and shows no similar songs.

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.

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.

Can you share more details about the "custom AI audio model" used to generate the embeddings?

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

I laughed a lot.

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.

Tried LSDREAM - Oblivion, and I get meditation music. Completely different and makes it hard to believe it "analyzes raw music audio as input"...

it is not the typical "song radio" approach that would provide more a popularity / artist similarity based playlist but a real "similar song" google like search. I think that is valuable to discover new music, especially to find unknown artists. But if someone were to embed it into streaming clients that would need to be a different feature too, probably more search related.

I tried a few of my favorites and the results were... predictable, I guess.

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

Seems like the recommendations are somewhat hit or miss so far. I would suggest finding a way to say why it recommended a particular song. I know that this is already common, but maybe you could come up with a new spin on it.

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 think you may have inadvertently just closed a loophole with iTunes and will be the one and only non-Apple employee to train an AI model on 120Million songs in the iTunes library. Further, Spotify employees/management will notice the success of this work and ensure it doesn't happen there. Please consider open sourcing your datasets.

Beautiful. Definetely could replace spotify "recommended" for me.

Please, add some filters:

- By language

- By year (between, above, below)

- By fame (so the user can filter out famous musics or unknown ones)

Thank you!

This is great! Found a few good songs I've never heard before.

Now, it would be awesome is it could generate playlists I could play directly in Apple Music.

Nice idea. Not sure how good the recommendations are just yet, but I really wish there was a Spotify button on there.

Also if it's not a trade secret can you expand "My model analyzes raw music audio as input and produces embedding vectors as output." What kind of analysis are you doing? What is the criteria for picking recommendations? I always find this area of music analysis intersting.

Fascinating stuff! DJing mainly electronic music, I tend to base a lot of my match-making on beat and rhythm. From playing with this AI model a little, I feel there is more priority placed on timbre than transient information? Really looking forward to seeing where this goes!

Interesting idea, I get pretty average recommendations though.

The first track I searched for was "Hazmat Modine - Bahamut" (https://maroofy.com/songs/253108933), it seems to recommend things with some similar instruments (e.g. brass and sax) but not really similar taste or style.

Great work, this is very fun! Alas, I tried “Werewolves of London” and it didn’t spit out “Sweet Home Alabama”, so it still has room to grow

I tried one of my own songs... I suppose it picked up that my stuff is a bit jazz-influenced, but it otherwise doesn't seem very similar. It tickles me that it thinks I'm similar to both Johnny Mathis and Willie Nelson, though. :)

https://maroofy.com/songs/899061474

It produces similar sounding music. but that's not really what one is looking for when discovering music. Example is when I put a Bollywood song, it produces for me similar sounding Latin music which isn't going to fall into a good queue.

how Spotify does this is matching songs that have been human added to the same playlists - hence matching tastes.

The idea is just great. Keep in mind what people expect from this type of website (and clear up any misconceptions), as this may lead to unnecessary churn. I would like to see some degree of customizability, e.g. weighting of features or something like that. (Nitpick: wrap the play svgs in a button tag like the "similar" button)

If I'm listening to a song, I don't want the next song to sound similar, and the next, and the next. For example, my playlist just played "I touch myself", "Whiskey in the Jar," and now "Somebody that I used to know." All of which sound entirely different. But, maybe I'm an outlier. Have fun!

I’m not able to paste into the song field, a little frustrating. Otherwise, this has caused me to sign up for Apple Music.

I’ve tried ‘Allan Holdsworth Sand’ and ‘Allan Holdsworth Non-brewed Condiment’ and results were completely unrelated, I would even say random. Then I’ve tried ‘Allan Holdsworth Sixteen Men of Tain’ and at least it found some jazz tracks. I guess a track needs to have a simple static rhythm to not throw off the algorithm.

How do you train “vibes” other than with human input, which cannot possibly scale to classify so many combinations

I’ve already discovered some cool bands with this. Much better than the big platform’s music recommendations. Love it!

Seems cool and I understand you're getting hugged to death by HN right now but I could not use it because the search was too slow and as soon as I change focus to another window for example, it removes whatever I wrote in the search bar so unfortunately completely useless due to this bug.

Bro, don't listen to the critics! It is amazing as it is. Much better than my spotify recommendatinos.

Searched for both "portishead" and "wandering star" and it just showed me songs or artists with those words in them, even if the music wasn't even remotely close ("lucero - wandering star" is very very different than "portishead - wandering star")

One of the files doesn't load right now

https://cdn.segment.com/v1/projects/F4GFNelOpRsgUJc6iwTuiXr2...

Error code: SSL_ERROR_RX_RECORD_TOO_LONG

Really cool! From my understanding, it looks like this is doing something like word embeddings and searching for nearby points in the embedding space.

Crazy idea: what if you used a dimensionality reduction like t-SNE instead of learning a vector representation? Would you expect similar results?

Interesting that you linked Hans Zimmer, as he's known for "reusing" material across OSTs. A particular example that comes to mind (and where the AI did not find a match) is The Battle (5:52) from the Gladiator OST vs the Pirates of the Carribean OST.

Dear @subtech I was curious if you tried other approaches, such as the same thing with lyrics instead of audio?

Also, you note "I’ve indexed ~120M+ songs from the iTunes catalog" -- could you share some of the libraries/tools you used and which you did or didnt like?

The homepage is an empty page with just one search box in the middle. It does not work in firefox.

I often want to search also by semantics of lyrics not only beat. I think there should be option for both.

EDIT: doesn't work for https://maroofy.com/songs/1640070887

Very cool, the results look great. Looking forward to throwing random songs in here going forward.

It's almost too good in that it finds a lot of song remixes which I don't necessarily want. So it's clearly targeting the right things in songs but I don't necessarily want to have a remixes. Might think of a way to adjust for that?

They'll either be sued into the ground or bought out for a multi-million figure by the Summer!

Good luck!

I have been wanting to do similar things. But, shied away because I wasn't in the mood to find out if this sort of thing was legal or not. Can someone here who knows this space better talk about the legal aspect of doing something like this?

I love the idea, but the results were far off the mark. Perhaps they should be weighted according to some vectors, including language.

I chose 'Let it be me' by the Everly Brothers. 8 or so of the top 10 were foreign songs, and sounded much older even.

Fun, but unfortunately it is pretty off for me compared to Spotify and Apple Music. I wouldn't listen to any of the recs personally. I do appreciate how out there the recommendations are though! They're just amateurish for my tastes.

I've just tried `Simon and Garfunkel - Mrs. Robinson` as the search input and the results are really compelling and interesting! Keep on the good work. I might use it to find new tracks for my daily playlist and dj sets!

How did you get the 120MM songs?

I found the recommended songs to be bang on similar to the ones I queried. Great job!

Are you taking bug reports here? This song seems to get no recommendations:

https://maroofy.com/songs/1634824510

Server error is:

Unexpected token < in JSON at position 0

https://maroofy.com/songs/500162235

Your AI is processing the tempo and beat of the music, its not looking at the lyrics and what the lyrics mean, its not processing any accompanying music videos and its not really factoring in emotion, the mood and events of the day in the locale it comes from.

Another factor, is its not taking into account the limitation instruments had on music before the introduction of the synthesizer and the tech we have available today in order to make music.

There are other websites which also make suggestions and doesn't factor in the above points, so your website is not alone in not really grasping the mood of the song. I'm sure its a limitation of the AI models available to people like yourself today, along with time constraints and other factors including access to data. Just how far do you read into things?

When you see an AI suggest the likes of the Sex Pistol's God Save the Queen with this song, you are then looking at an AI which is intelligent. Quantify the mood of the music, the mood of the country or region during that period of time. Thats a very hard thing to do, and masters of that would be the BBC, simply because as national broadcasters, an extension of the British Military aka MI7, arguably global broadcasters with their world service, you'll realise there are songs which just wont be played when certain events are happening on the ground in regions which would be featured in the news and thus considered inciteful. Its very subtle how manipulation at a national or global level takes place, but it takes place.

However it is also a very difficult song to quantify, as this youtube reaction video sums up nicely. https://youtu.be/5vK1FZO1ybE?t=296

If you know anything about the band, they span many genres, and yet they also their own genre. Truly Prodigious.

I quickly found some incredible music from this.

For example, by searching Gold Panda I found Major Dynamic https://majordynamic.bandcamp.com/

Thank you. This is really great. Of course not everything is a hit but it did find some beautiful tunes for me.

One little problem: if you press play and wander off after it stopped playing it can be rather hard to find what you played.

genuinely stoked it found a track that sounds like Cicatraz ESP by Mars Volta in Addiction by Tripp Berlin (which is new to me, so thanks for that!) but as others have said in other cases it fails to understand the pull of a given song but you can always understand why it recommended a given track.

One outcome I found interesting is that I gave it Ante Bijou Up but it didn't find any of the original versions of Ante Up based off of it and its first recommendation was some hideous pop song.

However, based off the success of the Cicatraz ESP recommendation I am a massive fan of your work! <3

Tested with my own music production and the first similar result was quite impressive!

https://maroofy.com/songs/1452423952

Wonderful idea and well designed interface.

Unfortunately failed quite spectacularly for the song I selected. I chose "Why Hide" by Mark Ronson and it somehow managed to pick the exact music I tend to dislike.

Laughably bad for "Here's Where The Story Ends" by The Sundays, I can't even begin to see how some of these recommendations relate. Did discover that Finnish rockabilly is a thing, though.

Looks like a cool project. As long as it is free and not being monetized, then it might keep Apple from unleashing their lawyers.

Apple might as well buy your project for iTunes since they are extremely behind in anything AI.

I'm not sure I really understand, how did you train it on 120,000,000 songs from Itunes? How do you have access to all those songs, piracy?

I searched for "Hot Chip" and all the recommendations were other Hot Chip songs... is that normal? I was expecting different artists, and honestly not expecting any songs by the same artist.

Needs an auto playlist generator! :) I would FUCKING KILL for that

Good idea - can use work on data cleaning though; got a lot of duplicate listings for some songs searched.

It’s like feeding Apple Music through Shazam - wonder why Shazam doesn’t do what you’ve done here

Just tried it - I Walk Up by Luke Christopher and Memories is not too bad! Admittedly - encountered quite a few problems as part of this though - with a few random pop songs in the list.

Wow, I love this! Can you make clicking play change the url so that songs are added to our browser history? Perhaps make "refreshed" pages idempotent via a hash in the url?

I think it’s an interesting idea and look forward to future work, but I don’t think the current results are much better than random.

I don’t really understand the features your model has learned but I’d start by trying to understand those features.

Music is highly structured and reasonably well organized already. Understanding if your model learned any features that map to metrics used by humans is an important question. If the answer is “no” then I’d wonder if it’s actually achieving anything beyond random number generation.

—-edit—- I went back and tried modern pop. Which I believe it has learned better, although all of the music there sounds so similar maybe that is just a result of the small sample I tried.

O.o you beat me to it I was thinking of building something similar

I don't think I could've done the UI as clean, though, very nice!

I hope the mainstream platforms start implementing stuff like this soon

Did you pay for the songs?

There is also something to read

https://schiptsov.github.io/GPT-bullshit.html

Would love to know more about how you produced the embeddings!

How does your approach compare to

https://news.ycombinator.com/item?id=34445585

Brilliant. Would be nice also to see what's playing. When I've scrolled and clicked a lot of songs and realize I like it, I have no idea what I clicked!

How did you extract the embeddings from the songs? Did you use a pre-trained model? Or have you trained you own model (using autoencoders or something similar)?

I tried it, I think the vibe match looks great, but the match lacks the culture context. It would be great to use song's meta data and lyrics to to improve that

this is great! can you turn this into a playlist or something?

like, i'll select couple of the songs and then it gets converted into a spotify playlist.

I need spotify btw, not apple music.

Oh and this page always bugs out:

https://maroofy.com/songs/56371517

I tried "norwegian wood" and got a lot of acoustic guitar with strong down/up strokes but nothing musically similar. Beatles they weren't.

Pretty cool. I think I broke it with Enya's Pax Deorum, though... can't get it to work at all now ("Hmm, something went wrong.")

Small tip: You should let it autoplay. Some recs are better than others and if I let it play in the background a good song will catch my attention.

Can you aggregate across artists? Given all the song-song relationships of a given artist, what other artist shows up frequently?

I guess my taste is outside the loop. Two songs off the top of my head I guess are not in it’s database:

Beautiful Strangers (Kevin Morby) Elephant Woman (Blonde Redhead)

What model/architecture did you use? Also curious about the process with that. Did you use an off the shelf model (like Jukebox) to embed the tracks?

I tried "learning to fly" by Pink Floyd. The results did not sound similar at all to me. Maybe I have non conventional idea on what is similar.

"I wrote AI to detect 4 chords songs"

https://youtu.be/5pidokakU4I

It works surprisingly well. Good job. It confuses bluegrass and latino music but they share tempo and instruments so I get it. Impressive.

This works great! I like the way it finds resonances between genres and am one of the folks that see that as a feature :-) Very well done!

It's fast.

But not sure if the song I input and the songs I get back are similar. Going to try them out further.

What things are they supposed to be similar at? beats? melody?

Dream Theater Space Dye Vest

-> returns christmas music

yeah I think your model missed the mark lol

It needs to go off the bulk of the song, not the first 20 second preview of the song...

I tried some songs but recommendations were tracks that sampled that song and songs with same name.

I am not sure if thats quite what i am looking for.

Awesome! You should name the model “Stealy Dan.”

Fun. I entered one of my own songs and the fifth result was "Love will tear us apart" by performed live by New Order.

It seems to be broken as of 08:44 UTC.

When i enter something in the search box i see "Loading..." and nothing happens.

I tried both Firefox and Safari.

I clicked one of the "recommended" songs on the front page, Du Hast - Rammstein. I got this:

Rhythm Is Gonna Get You Gloria Estefan

Could you please also add a "Compare" tab? So we can select two or more songs and see how similar they are?

I have no idea what metric the AI is using for 'similarity' but this just sounds like random results to me. None of the vocals, style, genre, and so on are in any way similar. When you look at recommendation engines on Spotify and YouTube music they do a much better job at this than this engine. I think maybe if you had access to data that users enjoy you could cross reference them to find patterns and use that to order your results.

Is this similar to how Shazam operates? I suppose there's more filtering and denoising going on with mic input.

I like the idea, but putting in some songs by Aphex Twin I didn't find the matches to be similar at all.

Reminds of last.fm and their scrobbler.

If would be amazing if it kept playing when you press “search similar” on the one you are playing.

Interesting job, How big are the vectors and did you use GPUs to trained? What models did you use?

Hey, I'd love to help you with this project. Feel free to reach out @duttaoindril

I find the list of similar songs to be bang on for a couple of songs I queried for. Great job!

This is great! So handy for the dj! Please keep up the good work, very appreciated!

Umm... is it just me or there's no "Search" button? Linux, Firefocks 50.1.

Very cool. Need a get a playlist from this. Seems like Spotify for us Apple Music peasants

Awesome! Curious how you got access to all the songs?! Did you legit pay for them all?

It doesn’t appear to work in Firefox. Looks like CORS is blocking a call to segment.

The search seems to be down? I'm not getting any results back in the dropdown.

This is cool. I’m curious how you managed to access all the music data from apple?

Really cool, quite surprising that this hasn't been done (well) before afaik.

Please remove the trackers so I can use search w/o using incognito mode.

Very cool, well done ! Are model code & weights open source?

interesting but it seems to just sound sonically similar songs.

Very cool, just tried it out and seems to work. Great job

Curious, did you just get the album/song lists from iTunes and get the corresponding raw music audio from other sources or does iTunes really allow one user account to download the raw audio of 120M songs?

How much does it cost to access this dataset at Apple?

very cool OP!

could you make an "auto playlist mode" that blends one song into another seamlessly?

usecase - party where we just want to set and forget one "vibe"

nice! is this in reference to composer javad maroofy?!

Which package/tool you used for semantic search?

Isn't Apple going to give you a hard time now? Years ago, Apple might have offered to hire you. Given what a jerk is Tim Cook, I imagine he'll have the lawyers send a cease and desist.

This works really well!

Could you add links to play songs on Spotify?

I see duplicates on recommendations list.

Loading … Beef it up before posting on HN?

Just wanted to say nice job!

Oh no AI replacing DJs.. :D good work, will use that next time I search for similar tracks, thanks!!

Can it do endless mashups?

Playlist button please!

this is pretty awesome.

needs a volume control

I wish it worked the results are horrible!!!

search is not working for me

Pandora?

Not even loading

My roofy?

I find it disheartening that the comments are loaded with people saying how bad it is. The interface is awesome, the ability to have search results pop up and offer suggestions is impressive. I thought the results for a Lana Del Ray song and a song by Carlos Santana were both interesting and suggested new things.

We should do a better job of supporting creators here.

Bad lyrics ruin songs for me, so I tend to listen to music in a way where I completely disregard the lyrics if they're bad, which causes me to like songs where the lyrics are hard to understand/hear or in a different language.

This seems to absolutely NAIL my case of listening, I'm loving this. Thank you!

Great initiative, but if it's sampling only the few seconds previews from the songs, then I doubt it will be useful. Also, a language filter is essential for both training the model and listing the results. I searched for a few Hindi language songs that I know of, and it listed only English, Chinese and Spanish songs as related ones - which is good to explore other music, but at times I'd just want to be in my native language zone and any other language would exercise my brain more than it'd relax it.

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Don't tell me, let me guess. All songs sound like English-language American pop songs.

(Sorry... I'm being a jerk here. This is a cool thing that you built. I only wish iTunes had a greater variety of music.)