Improving Tempo-Sensitive and Tempo-Robust Descriptors for Rhythmic Similarity.

André Holzapfel; Arthur Flexer; Gerhard Widmer

Improving Tempo-Sensitive and Tempo-Robust Descriptors for Rhythmic Similarity.

André Holzapfel
, Arthur Flexer
, Gerhard Widmer

Institute of Computational Perception

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

Abstract

For the description of rhythmic content of music signals usually features are preferred that are invariant in presence of tempo changes. In this paper it is shown that the importance of tempo depends on the musical context. For popular music, a tempo-sensitive feature is improved on multiple datasets using analysis of variance, and it is shown that also a tempo-robust description profits from the integration into the resulting processing framework. Important insights are given into optimal parameters for rhythm description, and limitations of current approaches are indicated.

Original language	English
Title of host publication	Proceedings of the 8th Sound and Music Computing Conference (SMC 2011), Padova, Italy.
Number of pages	6
Publication status	Published - 2011

Fields of science

102 Computer Sciences
102001 Artificial intelligence
102003 Image processing

JKU Focus areas

Computation in Informatics and Mathematics
Engineering and Natural Sciences (in general)

Cite this

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title = "Improving Tempo-Sensitive and Tempo-Robust Descriptors for Rhythmic Similarity.",

abstract = "For the description of rhythmic content of music signals usually features are preferred that are invariant in presence of tempo changes. In this paper it is shown that the importance of tempo depends on the musical context. For popular music, a tempo-sensitive feature is improved on multiple datasets using analysis of variance, and it is shown that also a tempo-robust description profits from the integration into the resulting processing framework. Important insights are given into optimal parameters for rhythm description, and limitations of current approaches are indicated.",

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AB - For the description of rhythmic content of music signals usually features are preferred that are invariant in presence of tempo changes. In this paper it is shown that the importance of tempo depends on the musical context. For popular music, a tempo-sensitive feature is improved on multiple datasets using analysis of variance, and it is shown that also a tempo-robust description profits from the integration into the resulting processing framework. Important insights are given into optimal parameters for rhythm description, and limitations of current approaches are indicated.

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