
Show HN: Momixa – Custom playlist mixes using machine learning - warpri81
https://momixa.com
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tomc1985
So... Pandora without all the song meta?

People make playlists for a variety of reasons, according to their specific
tastes. Songs that appear together on a functional playlist (workout jams,
study music, etc) likely won't have appeared together for the same (or even
similar reasons). Contrast that even further against playlists built for
aesthetic reasons ('sad songs', 'psychedelic-sounding', 'minimal', etc) and it
seems hard to imagine how mining just playlists can lead to good music
selections.

Is this algorithm attempting to track context or merely playlist contents?

And for the record, if there is one area that I really want machine learning
and "AI" to fall flat on its face with, it's music. It's a shame technophiles
want to strip the _people_ out of art and its consumption.

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visarga
> And for the record, if there is one area that I really want machine learning
> and "AI" to fall flat on its face with, it's music. It's a shame
> technophiles want to strip the people out of art and its consumption.

Already advocating affirmative action for people in arts? That must mean a lot
for "deep art" researchers.

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tomc1985
How is that 'affirmative action'?

I simply want "deep art" to fail. Big difference. A world dominated by AI-
generated art will coalesce us all into one or a handful of dull, lowest-
common-denominator aesthetics and rob the world of aesthetic vitality. Not to
mention all the artists, many of whom are already scraping for work, that get
shut out as peoples' artistic pursuits are gratified instantly.

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slyfocks
Apparently it has only trained on 30k songs thus far. This would explain the
bias towards pop, as 30k is far too small of a training set to accurately
capture the long tail of preferences.

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c3534l
I'm not always getting the greatest results. Mixing Portishead with
Perturbator gave me sensical results, but Perturbator with Dance With the Dead
should have given me other synthwave/outrun style faux-80s music. Instead it
gave me The Notorious BIG, Sound Garden, and the Foo Fighters.

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tahw
Huh! I also asked for something weird (Frank Zappa + Animal Collective) and
got Foo Fighters. I wonder if there's some vector math going on that makes Foo
Fighters crop up if the input songs are too orthogonal.

~~~
warpri81
You are probably correct about the vector math! We learn low level embeddings
from playlists we scraped from Spotify (with little discretion) and are still
working on the algorithm.

We are essentially trying to draw a line between the embeddings of the two
songs and find "close" songs to points along that line. This should give us a
somewhat smooth transition - at least that's the hope.

I suspect some of it may be that we are using a euclidean line through the
vector space, but using cosine distance for similarity. We're still trying to
get the hang of using the vectors to build a smooth transition between songs.

We are also tuning our model and training variables, as well as pulling in
more playlists, which should help (I hope).

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mrmondo
Hmm, got a lot of very poor recommendations when trying various realistic
combinations, the recommendation engine seems to promote pop music very
heavily and doesn’t seem to consider lesser played tracks fairly in its
analysis. Both Apple Music and pandora do a pretty good job of this by
comparison.

~~~
warpri81
This is true. I wish we had their entire playlist library!

This was trained on playlists we scraped from Spotify, so by definition pop
music is going to be heavily represented. We are still playing with the
training parameters and algorithm for building the playlists.

We also have some ideas on including "rare" or new tracks, but haven't gotten
to that part yet.

Thanks for checking it out!

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al3xnull
No idea where the feedback loop is for machine learning, but the playlists are
off by miles for anything other than popular genres of pop, (alt-)rock, and
some others. No Mozart or Beethoven; Couldn't match Arch Enemy on anything
heavy.

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warpri81
We use a shallow neural network to learn about songs from playlists we scraped
from Spotify, but to actually build the playlist we use some straightforward
vector math.

Rarer songs/genres do tend to be all over the map, and certain super popular
songs seem to show up all the time. We are still messing with the training,
pulling in more playlists, and working on the playlist algorithm. Every
training run produces better results!

From the comments, it does appear that we set the threshold for how frequently
a song must appear to be included a little low based on the sparsity of our
training data.

Thanks for taking the time to check it out!

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howderek
It did a good job of finding other artists that I like, but the playlist
didn't have any central theme or mood.

~~~
warpri81
Thanks for taking a look!

We basically try to draw a line between the two songs you chose and find songs
close to that line. It can produce some weird results, and it very seldom
seems to be the smooth transition we were shooting for.

We are still playing around with the algorithm for generating the playlist, as
well as getting more/better training data.

