Glenn McDonald

This is a good article that answers this question: https://www.furia.com/page.cgi?type=log&id=473

> This is problematic, clearly. A company needs to be able to treat its employees as interchangeable and expendable, both individually and collectively. It needs to be able to periodically layoff 17% of its workforce to cut its margin overhead by 1% and temporarily boost its stock price by 5%, without having to endure existential upheaval to its ongoing business processes.

as for the solution: basically I think the people who CAN be better managers keep saying, "oh I hate working with people, I'd rather code" and hence you're left with a very specific subset of managers.

> everybody is better off if companies ignore this caution with the same exuberant disregard as people doing their jobs with inadvisable devotion. The most transformational human ideas begin in individual hearts, whatever gantlets of brainstorming and strategic opportunity-analysis they subsequently have to run. Spotify was more right, I think, to tolerate my curiosities and experiments for 10 years than they were to finally give up on them out of exasperation or ignorance.

I mean, I'm pretty sure it is not just audio spectral features used, though I can't find a citation either way. (I am sure because the language distinctions make clear that we are at least making heavy use of metadata if not actual social patterns in listening, but the latter is explicitly heavily used elsewhere on the site, so...)

Also, if we're being exact, it's the work of Glenn McDonald under the auspices the Echo Nest, which is now owned by Spotify, but it isn't the be-all-end-all of how Spotify models genres under the hood.

What I was curious about is how /u/beezlebroxxxxxx perceived it to do at the metal genres, simply because Glenn McDonald is himself a metal genre fan (https://www.furia.com/), so I'd've expected that to work decently well.

They definitely do both, in the public recommendations API you can see vestiges of the old EchoNest acoustic properties along with some new ones they’ve come up with. It’s fun to play around with.

https://developer.spotify.com/documentation/web-api/reference/get-recommendations

The guy behind Every Noise at Once (engineer at EchoNest/Spotify until the recent layoffs), has some interesting thoughts about this topic:

https://www.furia.com/page.cgi?type=log&id=478

He’s quite biased towards not using ML or acoustic characteristics for recommendations. But even if you disagree it is interesting to hear about how things were working under the curtain (for daylist in this case).

I'm a huge Spotify fan and if you've found Discover weekly useful you might like the following other resources for discovering new music:

http://furia.com/newparticles/current.html

http://everynoise.com/engenremap.html

https://open.spotify.com/user/spotify/playlist/3rgsDhGHZxZ9sB9DQWQfuf

There's more, of course but I agree that many of the curated playlists lean towards popular music. I also follow /r/spotify on reddit and every now and then there's a good playlist on there.

See also http://everynoise.com/ which is a view into how Spotify classifies music.

The creator wrote about it here:

http://blog.echonest.com/post/52385283599/how-we-understand-music-genres

and writes a lot about it on their blog:

http://www.furia.com/page.cgi?terms=noise&type=search

Of course those are going in the other direction, not generating the classification from the data, but it's probably one of the best data sets as far as classifying existing music.