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Effects of Album and Artist Filters in Audio Similarity Computed for Very Large Music Databases

Effects of Album and Artist Filters in Audio Similarity Computed for Very Large Music Databases Arthur Flexer∗ and Dominik Schnitzer∗†∗ Austrian Research Institute for Artificial Intelligence Freyung 6/6 A-1010 Vienna, Austria arthur.flexer@ofai.at †Department of Computational Perception Johannes Kepler University Linz Altenberger Str. 69 A-4040 Linz, Austria dominik.schnitzer@jku.at Effects of Album and Artist Filters in Audio Similarity Computed for Very Large Music Databases In music information retrieval, one of the central goals is to automatically recommend music to users based on a query song or query artist. This can be done using expert knowledge (e.g., www.pandora.com), social meta-data (e.g., www.last.fm), collaborative filtering (e.g., www.amazon.com/mp3), or by extracting information directly from the audio (e.g., www.muffin.com). In audio-based music recommendation, a wellknown effect is the dominance of songs from the same artist as the query song in recommendation lists. This effect has been studied mainly in the context of genre-classification experiments. Because no ground truth with respect to music similarity usually exists, genre classification is widely used for evaluation of music similarity. Each song is labelled as belonging to a music genre using, e.g., advice of a music expert. High genre classification results indicate good similarity measures. If, in genre classification experiments, songs from the same artist are allowed in both training and test http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computer Music Journal MIT Press

Effects of Album and Artist Filters in Audio Similarity Computed for Very Large Music Databases

Flexer, Arthur; Schnitzer, Dominik
Computer Music Journal , Volume 34 (3) – Sep 1, 2010
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References (19)

Publisher
MIT Press
Copyright
© 2010 Massachusetts Institute of Technology.
Subject
Music Information Retrieval
ISSN
0148-9267
eISSN
1531-5169
DOI
10.1162/COMJ_a_00004
Publisher site
See Article on Publisher Site

Abstract

Arthur Flexer∗ and Dominik Schnitzer∗†∗ Austrian Research Institute for Artificial Intelligence Freyung 6/6 A-1010 Vienna, Austria arthur.flexer@ofai.at †Department of Computational Perception Johannes Kepler University Linz Altenberger Str. 69 A-4040 Linz, Austria dominik.schnitzer@jku.at Effects of Album and Artist Filters in Audio Similarity Computed for Very Large Music Databases In music information retrieval, one of the central goals is to automatically recommend music to users based on a query song or query artist. This can be done using expert knowledge (e.g., www.pandora.com), social meta-data (e.g., www.last.fm), collaborative filtering (e.g., www.amazon.com/mp3), or by extracting information directly from the audio (e.g., www.muffin.com). In audio-based music recommendation, a wellknown effect is the dominance of songs from the same artist as the query song in recommendation lists. This effect has been studied mainly in the context of genre-classification experiments. Because no ground truth with respect to music similarity usually exists, genre classification is widely used for evaluation of music similarity. Each song is labelled as belonging to a music genre using, e.g., advice of a music expert. High genre classification results indicate good similarity measures. If, in genre classification experiments, songs from the same artist are allowed in both training and test

Journal

Computer Music Journal – MIT Press

Published: Sep 1, 2010

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