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

• Type: Conference
• Year: 2009
• Where: SAC
• Authors: Tian-Jiang Wang, Gang Chen, Perfecto Herrera
• Keywords: Active Learning, Applied Computing, SAC 2009, Selection Strategy, Support Vector Machine