Algorithmic clustering of music based on string compression

Algorithmic clustering of music based on string compression (2004)

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by Ronald De Wolf, et al.

Venue:	COMPUTER MUSIC JOURNAL
Citations:	35 - 12 self

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Datum	Value	Source
TITLE	R.: Algorithmic clustering of music based on string compression	INFERENCE
AUTHOR NAME	Ronald De Wolf	SVM HeaderParse 0.2
AUTHOR AFFIL	; Institute for Logic, Language, and Computation; University of Amsterdam; {Rudi.Cilibrasi, Paul.Vitanyi,	SVM HeaderParse 0.2
AUTHOR ADDR	; Kruislaan 413; 1098 SJ Amsterdam, The Netherlands; Plantage Muidergracht 24; 1018 TV Amsterdam, The Netherlands	SVM HeaderParse 0.2
ABSTRACT	musical pieces are similar, but some are more similar than others. Apart from serving as an infinite source of discussion (‘‘Haydn is just like Mozart—No, he’s not!’’), such similarities are also crucial for the design of efficient music information retrieval systems. The amount of digitized music available on the Internet has grown dramatically in recent years, both in the public domain and on commercial sites; Napster and its clones are prime examples. Web sites offering musical content in some form like MP3, MIDI, or other, need a way to organize their wealth of material; they need to somehow classify their files according to musical genres and subgenres, putting similar pieces together. The purpose of such organization is to enable users to navigate to pieces of music they already know and like, but also to give them advice and recommendations (‘‘If you like this, you might also like...’’). Currently, such organization is mostly done manually by humans, or based on patterns in the purchasing behaviors of customers. However, some recent research has been examining the possibilities of automating music classification. A human expert, comparing different pieces of music with the goal of clustering similar works together, will generally look for certain specific similarities. Previous attempts to automate this process do the same. Generally speaking, they take a file containing a piece of music and extract from it various specific numerical features, related to pitch, rhythm, harmony, etc. One can extract such features using, for instance, Fourier transforms (Tzanetakis and Cook 2002) or wavelet transforms	SVM HeaderParse 0.2
YEAR	2004	INFERENCE
VENUE	Computer Music Journal	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	49--67	INFERENCE
CITATIONS	20 found	ParsCit 1.0