IEEE Transactions on Multimedia
A novel approach to musical genre classification using probabilistic latent semantic analysis model
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Semi-supervised Bayesian ARTMAP
Applied Intelligence
Modeling concept dynamics for large scale music search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Audio classification with low-rank matrix representation features
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Editorial: Partially supervised learning for pattern recognition
Pattern Recognition Letters
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In this paper, we propose an information fusion framework for the semi-supervised distance-based music genre classification problem. We make use of the regularized least-square framework as the basic classifier, which only involves the similarity scores among different music tracks. We present a similarity score that multiplies different scores based on different distance measures. Particularly the distance measures are not restricted to the Euclidean distance. By adding a weight to each single distance based score, we propose an expectation-maximization (EM) algorithm to adaptively learn the fusion scores. Experiments on real music data set show that our approach can give promising results.