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Bass Playing Style Detection based on High-Level Features and Pattern Similarity

: Abeßer, Jakob; Bräuer, Paul; Lukashevich, Hanna; Schuller, Gerald

Volltext (PDF; )

Downie, J.S.; Veltkamp, R.C. ; International Society for Music Information Retrieval -ISMIR-:
11th International Society for Music Information Retrieval Conference, ISMIR 2010 : Utrecht, August 9-13, 2010
ISBN: 978-90-393-5381-3
International Society for Music Information Retrieval Conference (ISMIR) <11,2010, Utrecht>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IDMT ()
music classification; music similarity

In this paper, we compare two approaches for automatic classification of bass playing styles, one based on highlevel features and another one based on similarity measures between bass patterns. For both approaches, we compare two different strategies: classification of patterns as a whole and classification of all measures of a pattern with a subsequent accumulation of the classification results. Furthermore, we investigate the influence of potential transcription errors on the classification accuracy, which tend to occur when real audio data is analyzed. We achieve best classification accuracy values of 60.8% for the feature-based classification and 68.5% for the classification based on pattern similarity based on a taxonomy consisting of 8 different bass playing styles.