Music Recognition and Classification Algorithm considering Audio Emotion

Abstract

At present, the existing music classification and recognition algorithms have the problem of low accuracy. Therefore, this paper proposes a music recognition and classification algorithm considering the characteristics of audio emotion. Firstly, the emotional features of music are extracted from the feedforward neural network and parameterized with the mean square deviation. Gradient descent learning algorithm is used to train audio emotion features. The neural network models of input layer, output layer, and hidden layer are established to realize the classification and recognition of music emotion. Experimental results show that the algorithm has good effect on music emotion classification. The data stream driven by the algorithm is higher than 55 MBbs, the anti-attack ability is 91%, the data integrity is 83%, the average accuracy is 85%, and it has good effectiveness and feasibility.