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A novel approach based on the probabilistic latent semantic analysis model (pLSA) for automatic musical genre classification is proposed in this paper. Unlike traditional usage, the pLSA is used to model musical genre instead of single music signal in the proposed approach. First, an unsupervised clustering algorithm is utilized to group temporal segments in music signals into several natural clusters. By this means, each music signal is decomposed into a bag of ldquoaudio wordsrdquo. Subsequently, the pLSA model of each musical genre is trained through a new iterative training procedure and well-known EM algorithm. This training procedure can iteratively update the pLSA model parameters by discriminatively computing weight of each training music signal and evidently improve the model's discriminative performance. Finally, these models can be used to classify new unseen music signals. Experiments on two commonly utilized databases show that our pLSA based approach can give promising results and the iterative learning procedure is effective.