IEEE Transactions on Multimedia
Combination of generative models and SVM based classifier for speech emotion recognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Sound indexing using morphological description
IEEE Transactions on Audio, Speech, and Language Processing
Music classification via the bag-of-features approach
Pattern Recognition Letters
Supervised dictionary learning for music genre classification
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Inferring personal traits from music listening history
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
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Temporal feature integration is the process of combining all the feature vectors in a time window into a single feature vector in order to capture the relevant temporal information in the window. The mean and variance along the temporal dimension are often used for temporal feature integration, but they capture neither the temporal dynamics nor dependencies among the individual feature dimensions. Here, a multivariate autoregressive feature model is proposed to solve this problem for music genre classification. This model gives two different feature sets, the diagonal autoregressive (DAR) and multivariate autoregressive (MAR) features which are compared against the baseline mean-variance as well as two other temporal feature integration techniques. Reproducibility in performance ranking of temporal feature integration methods were demonstrated using two data sets with five and eleven music genres, and by using four different classification schemes. The methods were further compared to human performance. The proposed MAR features perform better than the other features at the cost of increased computational complexity.