Temporal Feature Integration for Music Genre Classification
Temporal feature integration is the process of combining all the feature vectors in a time frame into a single feature vector in order to capture the relevant temporal information in the frame. 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 DAR and 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 significantly better than the other features without much increase in computational complexity.