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

Authors:
Tian-Jiang Wang;Gang Chen;Perfecto Herrera
Affiliations:
Huazhong University of Science and Technology, Wuhan, China;Huazhong University of Science and Technology, Wuhan, China;Universitat Pompeu Fabra, Barcelona, Spain
Venue:
Proceedings of the 2009 ACM symposium on Applied Computing
Year:
2009

Citing 3
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Abstract

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.