Thank heavens I can now listen to Bach without fearing he may not be great. Now if "maths" can prove da Vinci was a great artist and Michelangelo a great sculptor and Shakespeare a great writer!
> Now if "maths" can prove da Vinci was a great artist
Well there is that whole golden ratio thing, perhaps you've heard of it? While I personally find all of that a stretch, plenty of people find merit in it.
And so far as JS Bach is concerned, it's pretty clear he was a mathematical thinker even if it wasn't necessary deliberate in his composing. Maybe it was? I don't know and don't really care because a casual listen makes it obvious.
Western classical music at its core is built around Pythagorean temperament, so it stands to reason that some outgrowth of this system can be modeled using pure mathematics, does it not?
I realize there are people who have contempt for Western music for various reasons, but I'd tell them to hate the game, not the player... it's not the music's fault that crappy culture manifested.
> And so far as JS Bach is concerned, it's pretty clear he was a mathematical thinker even if it wasn't necessary deliberate in his composing. Maybe it was?
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).
I didn’t argue with your information-free tautologies about tools, I’m pointing out that they are straw man when it comes to using entropy to identify the quality level of music.
> network entropy doesn’t need to identify the quality level of music on it’s own to be useful.
Okay. Another context-free tautology. So what are we even talking about then, what is your point? You offered above “you could eg. test the vast swaths of potentially overlooked composers to see if any of them merit closer listening.” Are you taking back that suggestion?
Feel free to offer something - anything - more specific on how the entropy can provide useful information about music. What uses are you envisioning? What other metrics in combination with entropy are you thinking of?
What I don’t see in your argument is a single specific reason the specific paper we’re commenting on has value, and what that value is. You’re suggesting that someone else doing something else might someday uncover usefulness or applications, and maybe it will build on this paper. That could happen, and yet measuring entropy is already a well known idea, and the applications to compression have been well explored already, and we can demonstrate that entropy of music has no correlation with quality, therefore the probability of what you suggest actually happening still seems rather low, and the discussion doesn’t seem to be improving the odds.
You said I was using tautologies as straw men, which is incoherent and suggests you’re not arguing in good faith.
Anyhow, of course entropy correlates to music quality; maximum entropy music is white noise! I’ve even had luck finding interesting jazz musicians from the distribution of key signatures they use—- anything more entropic than the Real Book is a great indicator. Similarly, network entropy makes it easier to identify musicians with a flexible arsenal of riffs. You could adapt it to chord progressions to find unusual reharmonizations in live jazz to study and practice. It could be a helpful regularizer for neural network music generation. Entropic methods are among the most powerful in statistics.
It looks like we've hit a reply depth limit, which is maybe for the best, because I don't think we're making any progress here.
> You did use tautologies...
You seem to think calling something a tautology is a way to dismiss it. Almost everything in mathematics is a tautology-- most of what I say is a tautology. Any rigorous argument is tautological; it's the aspiration of literally all formal reasoning.
> Lots of uninteresting and bad music is also entropic.
And here, you seem to think someone is claiming that entropy is equivalent to music quality, not just a useful correlate or eg. indicator of something that might be more likely to show up in good music than bad music. I don't know of anyone making that claim; all the examples I gave require mild correlation.
You did use tautologies, and they are right there above and still irrelevant, and thus straw man arguments in the context of the question what useful information is this specific paper contributing to the corpus of knowledge. The irony of flinging bad faith accusations and ad-hominem when trying to distract from the failure to have a relevant argument isn’t lost on me though.
As you point out, white noise is more entropic than the Real Book. Lots of uninteresting and bad music is also entropic. Why exactly is that a good indicator? I’m glad you finally have some examples, but this doesn’t demonstrate that entropy is a decent discriminator of anything.
The paper definitely doesn't "prove that Bach was a great composer". It's about something completely different. The title of the referenced article is BS.
Was this in need of "proof"?
Read, for example http://www.ram.org/ramblings/books/the_eight.html
If only Neville had proof it would have been a much better novel.