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2009
ACM

Music retrieval based on a multi-samples selection strategy for support vector machine active learning

14 years 7 months ago
Music retrieval based on a multi-samples selection strategy for support vector machine active learning
In active learning based music retrieval systems, providing multiple samples to the user for feedback is very necessary. In this paper, we present a new multi-samples selection strategy designed for support vector machine active learning. Aiming to reduce the redundancy between the selected samples, the strategy enforces the selected samples to be diverse by explicitly maximizing the distance between each other in the feature space. Experimental results on a music genre database demonstrated the effectiveness of the proposed strategy in selecting relevant multiple samples for human feedback on them. Categories and Subject Descriptors H.5.5 [Sound and Music Computing]: Methodologies and techniques General Terms Algorithms, Design, Experimentation Keywords support vector machine, active learning, relevance feedback, selection strategy, music retrieval
Tian-Jiang Wang, Gang Chen, Perfecto Herrera
Added 19 May 2010
Updated 19 May 2010
Type Conference
Year 2009
Where SAC
Authors Tian-Jiang Wang, Gang Chen, Perfecto Herrera
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