You might be missing the point somewhat. With these methods, you could eg. test the vast swaths of potentially overlooked composers to see if any of them merit closer listening.
Very highly unlikely. The method in the paper isn’t measuring quality or novelty or authenticity or listenability or anything useful for evaluating composers without listening to them. It’s measuring the compressibility of MIDI files. We already know that Bach is less compressible than Philip Glass, and more compressible that Charles Ives. The methods in this paper cannot tell you if a composer is boring or derivative, nor whether they’re fresh and innovative for their time. They also can’t tell you anything about a performance. I mean go ahead and try, I’m all for experimenting, but I predict that trying to apply this paper to looking for overlooked composers will be an exercise in sifting through noise, more effort than searching manually, and spending time writing code instead of listening to good music.
I think (have never tried) that analyzing the harmony and rhythm (can you quantify syncopation?) you’d have a good start at determining if a song is worth listening to.
First, the methods of the paper don’t have to be a Mendelssohn replacement to be useful. Second, if you don’t like that potential application, consider all the other predictive models that could benefit from these features.
And likewise, you might be missing the point. This paper doesn’t really seem to add any useful knowledge to the corpus, and its methods are extremely unlikely to be useful at all for any of the purposes you are suggesting or imagining. We’ve already had gzip for a long time, and we already know it does not make a good predictive model for anything except storage space.
Like I would totally agree that there’s value in predictive models. I just don’t think the work we’re commenting on is one of those, nor headed toward making one.
My point is just that proving known facts can be useful and interesting.
As for the paper, network entropy and node heterogeneity seem to be perfectly sensible statistical concepts, and encode useful information. They also dovetail conveniently with powerful tools in machine learning. Criticizing this paper for lack of potential applications feels unreasonable.
You’re totally right in the abstract, those statements in your comment about concepts and tools are true if not tautological. What’s missing is that this paper provides no useful information about music or composers, and not does not prove anything nor demonstrate anything not already known and/or proven. It’s not a viable path to discriminating the quality of musical compositions, and I think we can prove that (I already suggested known counter-examples).