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Comment by rom-antics | Hacker News Reader

> Unfortunately, the resolution of the data that we collect in AcousticBrainz is not enough to be used in this type of machine learning, and so we were unable to try these new techniques using the data that we had available in the database.

Reading between the lines here -- AcousticBrainz can't store copies of any actual audio for copyright reasons. Instead they store various fingerprints and derived properties of the audio signal (example[1]). When the project started they didn't anticipate the right kind of input data required for these newest AI techniques, so to make progress they would have to throw away 7 years of data and start over their data acquisition from scratch. Instead they decided to shut it down.

Just another way the RIAA is making the world a worse place to live.

[1] https://acousticbrainz.org/api/v1/6df2d3b5-25e1-4d4c-a196-04...

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Our decision to cancel this project has zero to do with the RIAA. All the reasons are in the blog post -- it is that simple.

The hardest thing you can do when you're knee deep in a project is to pull the plug. I've been there a few times and it takes a lot of courage to do, especially when other people have put a ton of time and energy into it.

I'm sorry it didn't work out.

Is there a possible pivot to something else non-music related, or adapting your technology to another industry?

According to TFA, even if they had copies of every song they'd ever analyzed they'd still need to re-start from scratch. The reason they're not is:

"…we don’t have the resources and developer availability to perform this kind of research ourselves, and so we rely on the assistance of other researchers and volunteers to help us integrate new tools into AcousticBrainz, which is a relationship that we haven’t managed to build over the last few years."

If the RIAA was to blame, Pandora's Music Genome Project and other music analysis/recommendation systems wouldn't exist.

I know Spotify does a lot of analysis and clever things with the music on their platform, but there's the kicker, they actually have licenses and whatnot to the music, which I'm sure includes a clause for statistical / data-driven analysis.

I wonder if that license extends to deriving a ML model from it. I know there's some ML models out there already that can produce music based on a prompt, but that'll be limited to what music the authors have available to them. Spotify (and Apple Music, and the others) have millions upon millions of tracks available to them.

> I know Spotify does a lot of analysis and clever things with the music on their platform

Do they?

I have ‘melancholic’ and ‘I want to hype myself up’ playlists. Both from the tone of the music, voice, and lyrics (even the titles, I’d say) it is extremely obvious what the overarching vibe of the playlist is. Yet the radio-mode of those playlist or the recommendations below the playlist are fairly obviously just “other people who played the songs in this playlist also liked these songs”.

As far as I can tell there are no deep analysis smarts at work, or even something like EveryNoiseAtOnce.

I think your main point is correct, but the Music Genome Project had experts humans listening to each and every song. It originally started out to build music-store recommendation kiosks (yes kids, music used to be sold in physical stores), and it took them 5 years to pivot to streaming audio. Given when they started, I expect that they just bought a lot of CDs to start. By the time they got into streaming, they of course had licenses. So Pandora didn't defy RIAA; they spent money and built business relationships, two things unavailable here.

I suspect the RIAA might even be favor of supporting recommendation services—after all, matching people to music they like promotes music sales. RIAA members have a strongly vested interest in selling more music.

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