Ahrendt, Peter

Hansen, Lars Kai

Larsen, Jan

Meng, Anders

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

Online Research Database In Technology

Temporal feature integration for music genre classification

Anders Meng

Peter Ahrendt

Jan Larsen

Lars Kai Hansen

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Temporal Feature Integration for Music Genre Classification

http://orbit.dtu.dk/files/3841539/Meng.pdf

Temporal feature integration for music genre classification

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Online Research Database In Technology

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