Music Emotion Classification Method Using Improved Deep Belief Network

Abstract

Aiming at the problems of difficult data feature selection and low classification accuracy in music emotion classification, this study proposes a music emotion classification algorithm based on deep belief network (DBN). The traditional DBN network is improved by adding fine-tuning nodes to enhance the adjustability of the model. Then, two music data features, pitch frequency and band energy distribution, are fused as the input of the model. Finally, the support vector machine (SVM) classification algorithm is used as a classifier to realize music emotion classification. The fusion algorithm is tested on real datasets. The results show that the fused feature data of pitch frequency and band energy distribution can effectively represent music emotion. The accuracy of the improved DBN network fused with the SVM classification algorithm for music emotion classification can reach 88.31%, which shows good classification accuracy compared with the existing classification methods.