There is a whole field of music classification, if anybody (except copyright holders) were interested in using that, I'd expect it to be the likes of Spotify.
The problem with classification is that what makes a genre is not uniform. Some genres are defined by the way people sing, other genres are defined by the singers language, other are purely about the instrumentation or rhythms used, yet others mostly about the sounds and notes used etc.
But there are things like tempograms, tonnetz (tonal centroid features), chromagrams, spectral flatness/contrast/roloff, laplacian segmentation etc. And I guess feeding these into some neural net might give you interesting results.
What someone likes also doesn’t correlate solely with genres. While I like certain genres more than others, I only really like a small fraction of pieces in each genre, so the statistical correlation between what I like and genre affiliation is probably not very high.
The problem with classification is that what makes a genre is not uniform. Some genres are defined by the way people sing, other genres are defined by the singers language, other are purely about the instrumentation or rhythms used, yet others mostly about the sounds and notes used etc.
But there are things like tempograms, tonnetz (tonal centroid features), chromagrams, spectral flatness/contrast/roloff, laplacian segmentation etc. And I guess feeding these into some neural net might give you interesting results.