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A friend of mine did his PhD thesis on automated music synthesis and as a subproject came up with a model to study music transcriptions that he got from the Pea

by gomox 10y ago

A friend of mine did his PhD thesis on automated music synthesis and as a subproject came up with a model to study music transcriptions that he got from the Peachnote corpus [1]. One of his preliminary results was that his algorithms successfully "discovered" 4 big classical music movements on their own, i.e. without any prior labelling or classification, by using clustering algorithms. He posted about it on his blog with a link to his paper [2]. I always had a hard time explaining to non-computer people how amazing that seems. [1] http://www.peachnote.com/ http://www.peachnote.com/ [2] http://pablozivic.com.ar/post/51774763596/perceptual-basis-of-evolving-western-musical http://pablozivic.com.ar/post/51774763596/perceptual-basis-o...

derefr 10y ago
It doesn't seem all that amazing to me. Humans have to be trained to perceive the differences in complex streams of information; by default, they just perceive the low-level features—a "wall of noise." Machine-learning algorithms, meanwhile, can use general techniques to notice information-theoretic properties of various pieces of data. Effectively, computers can "do statistical aggregation" about as effortlessly as humans "do hierarchical knowledge representation." And, in an information-theoretic sense, the different "trends" throughout the history of music look different under statistical analysis. They're complex in different ways; they have different "lumps"; different aspects of them can or cannot be compressed together as redundancies. If you would like to see this for yourself, simply feed a raw melodic note-structure (not embedded in XML or anything) to any modern dumb compression algorithm, and then look at the result in a hex editor, while also having the source still open. You should quickly be able to recognize the "transformational signature" that characterizes something like a chaconne, vs. something like a sonata.

by gomox 10y ago

derefr 10y ago

It doesn't seem all that amazing to me. Humans have to be trained to perceive the differences in complex streams of information; by default, they just perceive the low-level features—a "wall of noise." Machine-learning algorithms, meanwhile, can use general techniques to notice information-theoretic properties of various pieces of data. Effectively, computers can "do statistical aggregation" about as effortlessly as humans "do hierarchical knowledge representation." And, in an information-theoretic sense, the different "trends" throughout the history of music look different under statistical analysis. They're complex in different ways; they have different "lumps"; different aspects of them can or cannot be compressed together as redundancies. If you would like to see this for yourself, simply feed a raw melodic note-structure (not embedded in XML or anything) to any modern dumb compression algorithm, and then look at the result in a hex editor, while also having the source still open. You should quickly be able to recognize the "transformational signature" that characterizes something like a chaconne, vs. something like a sonata.