From Improved Auto-taggers to Improved Music Similarity Measures.

Klaus Seyerlehner; Reinhard Sonnleitner; Markus Schedl; David Hauger; B. Ionescu

From Improved Auto-taggers to Improved Music Similarity Measures.

Klaus Seyerlehner
, Reinhard Sonnleitner
, Markus Schedl
, David Hauger
, B. Ionescu

Institute of Computational Perception

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

Abstract

This paper focuses on the relation between automatic tag prediction and music similarity. Intuitively music similarity measures based on auto-tags should profit from the improvement of the quality of the underlying audio tag predictors. We present classification experiments that verify this claim. Our results suggest a straight forward way to further improve content-based music similarity measures by improving the underlying auto-taggers.

Original language	English
Title of host publication	Proceedings of the 10th International Workshop on Adaptive Multimedia Retrieval (AMR 2012)
Number of pages	10
Publication status	Published - Oct 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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title = "From Improved Auto-taggers to Improved Music Similarity Measures.",

abstract = "This paper focuses on the relation between automatic tag prediction and music similarity. Intuitively music similarity measures based on auto-tags should profit from the improvement of the quality of the underlying audio tag predictors. We present classification experiments that verify this claim. Our results suggest a straight forward way to further improve content-based music similarity measures by improving the underlying auto-taggers.",

author = "Klaus Seyerlehner and Reinhard Sonnleitner and Markus Schedl and David Hauger and B. Ionescu",

year = "2012",

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booktitle = "Proceedings of the 10th International Workshop on Adaptive Multimedia Retrieval (AMR 2012)",

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AU - Sonnleitner, Reinhard

AU - Schedl, Markus

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AU - Ionescu, B.

PY - 2012/10

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N2 - This paper focuses on the relation between automatic tag prediction and music similarity. Intuitively music similarity measures based on auto-tags should profit from the improvement of the quality of the underlying audio tag predictors. We present classification experiments that verify this claim. Our results suggest a straight forward way to further improve content-based music similarity measures by improving the underlying auto-taggers.

AB - This paper focuses on the relation between automatic tag prediction and music similarity. Intuitively music similarity measures based on auto-tags should profit from the improvement of the quality of the underlying audio tag predictors. We present classification experiments that verify this claim. Our results suggest a straight forward way to further improve content-based music similarity measures by improving the underlying auto-taggers.

M3 - Conference proceedings

BT - Proceedings of the 10th International Workshop on Adaptive Multimedia Retrieval (AMR 2012)

ER -