MIREX 2012 Audio Beat Tracking Evaluation: Beat.E

Florian Krebs; Gerhard Widmer

MIREX 2012 Audio Beat Tracking Evaluation: Beat.E

Florian Krebs
, Gerhard Widmer

Institute of Computational Perception

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

Abstract

In this paper, we present a Hidden Markov Model (HMM) based beat tracking system that simultaneously extracts downbeats, beat times, tempo, meter and rhythmic patterns. Our model builds upon the basic structure proposed by Whiteley et. al [9], which we further modified by introducing a new observation model: rhythmic patterns are learned directly from data, which makes the model adaptable to the rhythmical structure of any kind of music. The MIREX beat tracking evaluation - 30 results using ten measures and three datasets - placed our algorithm among the top three performing algorithms thirteen times and always inside the top ten.

Original language	English
Title of host publication	Proceedings of the 13th International Society for Music Information
Number of pages	5
Publication status	Published - 2012

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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abstract = "In this paper, we present a Hidden Markov Model (HMM) based beat tracking system that simultaneously extracts downbeats, beat times, tempo, meter and rhythmic patterns. Our model builds upon the basic structure proposed by Whiteley et. al [9], which we further modified by introducing a new observation model: rhythmic patterns are learned directly from data, which makes the model adaptable to the rhythmical structure of any kind of music. The MIREX beat tracking evaluation - 30 results using ten measures and three datasets - placed our algorithm among the top three performing algorithms thirteen times and always inside the top ten.",

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AB - In this paper, we present a Hidden Markov Model (HMM) based beat tracking system that simultaneously extracts downbeats, beat times, tempo, meter and rhythmic patterns. Our model builds upon the basic structure proposed by Whiteley et. al [9], which we further modified by introducing a new observation model: rhythmic patterns are learned directly from data, which makes the model adaptable to the rhythmical structure of any kind of music. The MIREX beat tracking evaluation - 30 results using ten measures and three datasets - placed our algorithm among the top three performing algorithms thirteen times and always inside the top ten.

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