Matching Techniques for Large Music Databases

Matching Techniques for Large Music Databases (1999)

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by Alexandra Uitdenbogerd , Justin Zobel

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Datum	Value	Source
TITLE	Melodic matching techniques for large music databases	INFERENCE
AUTHOR NAME	Ra Uitdenbogerd	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Computer Science, RMIT University; GPO Box 2476V, Melbourne 3001	SVM HeaderParse 0.2
AUTHOR ADDR	Australia	SVM HeaderParse 0.1
AUTHOR NAME	Justin Zobel	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Computer Science, RMIT University; GPO Box 2476V, Melbourne 3001	SVM HeaderParse 0.2
AUTHOR ADDR	Australia	SVM HeaderParse 0.1
ABSTRACT	With the growth in digital representations of music, and of music stored in these representations, it is increasingly attractive to search collections of music. One mode of search is by similarity, but, for music, similarity search presents several difficulties: in particular, deciding what part of the music is likely to be perceived as the theme by a listener, and deciding whether two pieces of music with different sequences of notes represent the same theme. In this paper we propose a three-stage framework for matching pieces of music. We use the framework to compare a range of techniques for determining whether two pieces of music are similar, by experimentally testing their ability to retrieve different transcriptions of the same piece of music from a large collection of MIDI files. These experiments show that different comparison techniques differ widely in their effectiveness; and that, by instantiating the framework with appropriate music manipulation and comparison techniques, pieces of music that match a query can be identified in a large collection. 1	SVM HeaderParse 0.1
YEAR	1999	INFERENCE
VENUE	In Proceedings of ACM International Multimedia Conference (ACMMM	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	57--66	INFERENCE
CITATIONS	28 found	ParsCit 1.0