[macrolocal]

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.

[dahart]

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).

[macrolocal]

Of course network entropy provides useful information—- just maybe not the particular kind of depth you seem to be looking for.

I’m also curious why you’re arguing with my tautologies!

[dahart]

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.

[macrolocal]

Tautologies don't make for good strawmen, and network entropy doesn't need to identify the quality level of music on its own to be useful.

[dahart]

> Tautologies don’t make for good strawmen

Agreed! So what are you using them for?

> 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.

[macrolocal]

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.