Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41347
Title: Effectiveness of signal segmentation for music content representation
Authors: Maddage, N.C.
Kankanhalli, M.S. 
Li, H.
Keywords: Chord detection
Music segmentation
Vocal and instrumental regions
Issue Date: 2008
Source: Maddage, N.C.,Kankanhalli, M.S.,Li, H. (2008). Effectiveness of signal segmentation for music content representation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4903 LNCS : 477-486. ScholarBank@NUS Repository.
Abstract: In this paper we compare the effectiveness of rhythm based signal segmentation technique with the traditional fixed length segmentation for music contents representation. We consider vocal regions, instrumental regions and chords which represent the harmony as different classes of music contents to be represented. The effectiveness of segmentation for music content representation is measured based on intra class feature stability, inter class high feature deviation and class modeling accuracy. Experimental results reveal music content representation is improved with rhythm based signal segmentation than with fixed length segmentation. With rhythm based segmentation, vocal and instrumental modeling accuracy and chord modeling accuracy are improved by 12% and 8% respectively. © Springer-Verlag Berlin Heidelberg 2008.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/41347
ISBN: 3540774076
ISSN: 03029743
Appears in Collections:Staff Publications

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