Combining Features Reduces Hubness in Audio Similarity.

Arthur Flexer; Dominik Schnitzer; M. Gasser; Tim Pohle

Combining Features Reduces Hubness in Audio Similarity.

Arthur Flexer, Dominik Schnitzer, M. Gasser, Tim Pohle

Institute of Computational Perception

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

In audio based music similarity, a well known effect is the existence of hubs, i.e. songs which appear similar to many other songs without showing any meaningful perceptual similarity. We verify that this effect also exists in very large databases (> 250000 songs) and that it even gets worse with growing size of databases. By combining different aspects of audio similarity we are able to reduce the hub problem while at the same time maintaining a high overall quality of audio similarity.

Original language	English
Title of host publication	Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010)
Number of pages	6
Publication status	Published - 2010

Fields of science

102 Computer Sciences
102001 Artificial intelligence
102003 Image processing
102015 Information systems
202002 Audiovisual media

Cite this

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title = "Combining Features Reduces Hubness in Audio Similarity.",

abstract = "In audio based music similarity, a well known effect is the existence of hubs, i.e. songs which appear similar to many other songs without showing any meaningful perceptual similarity. We verify that this effect also exists in very large databases (> 250000 songs) and that it even gets worse with growing size of databases. By combining different aspects of audio similarity we are able to reduce the hub problem while at the same time maintaining a high overall quality of audio similarity.",

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AB - In audio based music similarity, a well known effect is the existence of hubs, i.e. songs which appear similar to many other songs without showing any meaningful perceptual similarity. We verify that this effect also exists in very large databases (> 250000 songs) and that it even gets worse with growing size of databases. By combining different aspects of audio similarity we are able to reduce the hub problem while at the same time maintaining a high overall quality of audio similarity.

M3 - Conference proceedings

BT - Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010)

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