Improving Prototypical Artist Detection by Penalizing Exorbitant Popularity

Markus Schedl; Gerhard Widmer; Peter Knees

Improving Prototypical Artist Detection by Penalizing Exorbitant Popularity

Markus Schedl, Gerhard Widmer, Peter Knees

Institute of Computational Perception

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

Abstract

Discovering artists that can be considered as prototypes for particular genres or styles of music is a challenging and interesting task. Based on preliminary work, we elaborate an improved approach to rank artists according to their prototypicality. To calculate such a ranking, we use asymmetric similarity matrices obtained via co-occurrence analysis of artist names on web pages. In order to avoid distortions of the ranking due to ambiguous artist names, e.g. bands whose names equal common speech words (like Kiss or Bush), we introduce a penalization function. Our approach is demonstrated on a data set containing 224 artists from 14 genres.

Original language	English
Title of host publication	In Proceedings of the 3rd International Conference on Computer Music Modeling and Retrieval (CMMR 2005), Pisa, Italy
Publisher	Berlin: Springer Verlag
Number of pages	5
Publication status	Published - 2005

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 = "Improving Prototypical Artist Detection by Penalizing Exorbitant Popularity",

abstract = "Discovering artists that can be considered as prototypes for particular genres or styles of music is a challenging and interesting task. Based on preliminary work, we elaborate an improved approach to rank artists according to their prototypicality. To calculate such a ranking, we use asymmetric similarity matrices obtained via co-occurrence analysis of artist names on web pages. In order to avoid distortions of the ranking due to ambiguous artist names, e.g. bands whose names equal common speech words (like Kiss or Bush), we introduce a penalization function. Our approach is demonstrated on a data set containing 224 artists from 14 genres.",

author = "Markus Schedl and Gerhard Widmer and Peter Knees",

year = "2005",

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Improving Prototypical Artist Detection by Penalizing Exorbitant Popularity. / Schedl, Markus ; Widmer, Gerhard; Knees, Peter.
In Proceedings of the 3rd International Conference on Computer Music Modeling and Retrieval (CMMR 2005), Pisa, Italy. Berlin: Springer Verlag, 2005.

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

TY - GEN

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AU - Widmer, Gerhard

AU - Knees, Peter

PY - 2005

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N2 - Discovering artists that can be considered as prototypes for particular genres or styles of music is a challenging and interesting task. Based on preliminary work, we elaborate an improved approach to rank artists according to their prototypicality. To calculate such a ranking, we use asymmetric similarity matrices obtained via co-occurrence analysis of artist names on web pages. In order to avoid distortions of the ranking due to ambiguous artist names, e.g. bands whose names equal common speech words (like Kiss or Bush), we introduce a penalization function. Our approach is demonstrated on a data set containing 224 artists from 14 genres.

AB - Discovering artists that can be considered as prototypes for particular genres or styles of music is a challenging and interesting task. Based on preliminary work, we elaborate an improved approach to rank artists according to their prototypicality. To calculate such a ranking, we use asymmetric similarity matrices obtained via co-occurrence analysis of artist names on web pages. In order to avoid distortions of the ranking due to ambiguous artist names, e.g. bands whose names equal common speech words (like Kiss or Bush), we introduce a penalization function. Our approach is demonstrated on a data set containing 224 artists from 14 genres.

M3 - Conference proceedings

BT - In Proceedings of the 3rd International Conference on Computer Music Modeling and Retrieval (CMMR 2005), Pisa, Italy

PB - Berlin: Springer Verlag

ER -