Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Robustness in Automatic Speech Recognition: Fundamentals and Applications
Robustness in Automatic Speech Recognition: Fundamentals and Applications
Fast Recognition of Musical Genres Using RBF Networks
IEEE Transactions on Knowledge and Data Engineering
Aggregate features and ADABOOST for music classification
Machine Learning
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Musical Genre Classification Using Nonnegative Matrix Factorization-Based Features
IEEE Transactions on Audio, Speech, and Language Processing
Toward intelligent music information retrieval
IEEE Transactions on Multimedia
Content-Based Information Fusion for Semi-Supervised Music Genre Classification
IEEE Transactions on Multimedia
Fusing audio vocabulary with visual features for pornographic video detection
Future Generation Computer Systems
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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 "audio words". 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.