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Comment by Cthulhu_ | Hacker News Reader

I dunno, I like the human touch; music genres aren't an exact science (is this blackened death metal or deathened black metal? Is there a difference?). I mean the data is there; see https://everynoise.com/, probably linked elsewhere, for a very big list of music genres + examples that IIRC come from Spotify's data lake. That can be used to train a model, I suppose.

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I'd like to see a recommendation program that trains on the many, many aspects of music itself that a person listens to. Rather than artists/labels/style slots/norms/years of release/charts ... it'd analyze your preferences in tempo, melody, harmony, instruments (type and number), vocal styles, novelty. (Getting started would take a while, say, over a month.)

Then to refine its model, it'd have you rate some picks (old and new based on what it knows so far), and analyze how your responses fit its model. Would it get better faster for people who like a wide variety, or those with a few specialities?

What you want is the model to be so advanced that it would probably be capable of just generating music. And actually if all you want is just similarly sounding music streaming into your ears then automatic generation is the natural continuation after automated recommendations.

I have always thought that instrumentation alone would be a better classification than genre.

That is Pandora's entire business model. They (used to?) have music majors review every track that came in on a bunch of criteria.

Sadly it never seemed to really scale and even to this day Pandora doesn't recommend the same broad swaths of music that Spotify does, but Pandora's recommendations tend to be more on point.

There was a brief window when Pandora was available in Canada (long ago now), but I still remember how good their recommendations were. Nothing matches it even today

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