Music mood and theme classification

A hybrid approach

authored by: Kerstin Bischoff, Claudiu S. Firan, Raluca Paiu, Wolfgang Nejdl, Cyril Laurier, Mohamed Sordo
Abstract: Music perception is highly intertwined with both emotions and context. Not surprisingly, many of the users' information seeking actions aim at retrieving music songs based on these perceptual dimensions - moods and themes, expressing how people feel about music or which situations they associate it with. In order to successfully support music retrieval along these dimensions, powerful methods are needed. Still, most existing approaches aiming at inferring some of the songs' latent characteristics focus on identifying musical genres. In this paper we aim at bridging this gap between users' information needs and indexed music features by developing algorithms for classifying music songs by moods and themes. We extend existing approaches by also considering the songs' thematic dimensions and by using social data from the Last.fm music portal, as support for the classification tasks. Our methods exploit both audio features and collaborative user annotations, fusing them to improve overall performance. Evaluation performed against the AllMusic.com ground truth shows that both kinds of information are complementary and should be merged for enhanced classification accuracy.
Organisation(s): L3S Research Centre
External Organisation(s): Universität Pompeu Fabra (UPF)
Type: Conference contribution
Pages: 657-662
No. of pages: 6
Publication date: 2009
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Music, Information Systems

BibTeX

@inproceedings{54b4d0b158554a80b4641bea12244d9a,
title = "Music mood and theme classification: A hybrid approach",
abstract = "Music perception is highly intertwined with both emotions and context. Not surprisingly, many of the users' information seeking actions aim at retrieving music songs based on these perceptual dimensions - moods and themes, expressing how people feel about music or which situations they associate it with. In order to successfully support music retrieval along these dimensions, powerful methods are needed. Still, most existing approaches aiming at inferring some of the songs' latent characteristics focus on identifying musical genres. In this paper we aim at bridging this gap between users' information needs and indexed music features by developing algorithms for classifying music songs by moods and themes. We extend existing approaches by also considering the songs' thematic dimensions and by using social data from the Last.fm music portal, as support for the classification tasks. Our methods exploit both audio features and collaborative user annotations, fusing them to improve overall performance. Evaluation performed against the AllMusic.com ground truth shows that both kinds of information are complementary and should be merged for enhanced classification accuracy.",
author = "Kerstin Bischoff and Firan, {Claudiu S.} and Raluca Paiu and Wolfgang Nejdl and Cyril Laurier and Mohamed Sordo",
year = "2009",
language = "English",
isbn = "9780981353708",
series = "Proceedings of the 10th International Society for Music Information Retrieval Conference, ISMIR 2009",
pages = "657--662",
booktitle = "Proceedings of the 10th International Society for Music Information Retrieval Conference, ISMIR 2009",
note = "10th International Society for Music Information Retrieval Conference, ISMIR 2009 ; Conference date: 26-10-2009 Through 30-10-2009",
}