A Middle-Level Learning Feature Interaction Method with Deep Learning for Multi-Feature Music Genre Classification

Nowadays, music genre classification is becoming an interesting area and attracting lots of research attention. Multi-feature model is acknowledged as a desirable technology to realize the classification. However, the major branches of multi-feature models used in most existed works are relatively independent and not interactive, which will result in insufficient learning features for music genre classification. In view of this, we exploit the impact of learning feature interaction among different branches and layers on the final classification results in a multi-feature model. Then, a middle-level learning feature interaction method based on deep learning is proposed correspondingly. Our experimental results show that the designed method can significantly improve the accuracy of music genre classification. The best classification accuracy on the GTZAN dataset can reach 93.65%, which is superior to most current methods.

Automated summary

Music genre classification has become an interesting research area, with the multi-feature model considered as an ideal technology for classification. In this study, a method based on deep learning that focuses on intermediate-level feature interaction is proposed, which significantly improves the accuracy of music genre classification. Experimental results show that this method outperforms most current methods, with a classification accuracy of up to 93.65% on the GTZAN dataset.