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OFAI-TR-2005-06

Evaluation of Frequently Used Audio Features for Classification of Music Into Perceptual Categories

Tim Pohle, Elias Pampalk, Gerhard Widmer

The ever-growing amount of available music induces an increasing demand for Music Information Retrieval (MIR) applications such as music recommendation applications or automatic classification algorithms. When audio-based, a crucial part of such systems are the audio feature extraction routines. In this paper, we evaluate how well a variety of combinations of feature extraction andmachine learning algorithms are suited to classifymusic into perceptual categories. The examined categorizations are perceived tempo, mood (happy / neutral /sad), emotion (soft / neutral / aggressive), complexity, and vocal content. The aim is to contribute to the investigation which aspects of music are not captured by the common audio descriptors; from our experiments we can conclude that most of the examined categorizations are not captured well. This indicates that more research is needed on alternative (possibly extra-musical) sources of information for useful music classification.

Keywords: AI-Austria Publication, Publications List AI (IMKAI+OESGK+OEFAI), WWW_ML_Music, WWW_ML_MIR, WWW_ML_ML, WWW_ML_SIMAC, WWW_ML_START

Citation: Pohle T., Pampalk E., Widmer G.: Evaluation of Frequently Used Audio Features for Classification of Music Into Perceptual Categories. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2005-06, 2005