Roland0 wrote: 
> That may be more involved than you'd expect.
> - first, you'd need to identify the audio features suited to this task
> (see 'A Review of Physical and Perceptual Feature Extraction Techniques
> for Speech, Music and Environmental Sounds'
> (https://www.mdpi.com/2076-3417/6/5/143/htm) for an overview, and 'this
> page' (https://essentia.upf.edu/streaming_extractor_music.html) for
> essentia_streaming_extractor_music's capabilities)
> - then, you'd find a way to compute the similarity for each feature
> (e.g. musly uses the Kullback-Leibler divergence for MFCCs)
> - finally, you'd have to combine the similarity of all features and find
> a way to compare songs
> 
> Note that if you use essentia_streaming_extractor_music, you'd also have
> to configure it to store all frame values (by default, only the
> statistics computed over frames are included in the output). At this
> point, using Essentia's API directly would make more sense.

Thanks, but that is probably a bit too much for me!

Think I'll stick with the high-level descriptors. I've analysed ~20k
tracks - which took quite some time at roughly 15 tracks/min (still
quicker than MIP). To locate 'similar' tracks I'm checking the
difference in 'danceable', 'aggressive', 'electronic', etc between a
seed track and tracks in the DB. Tracks are then sorted based upon
average difference of these parameters, and a random selection of the
top few chosen. This seems to work reasonably well for me. Code is not
ready for release, as I'm still playing with it - but can be found in my
github page.



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