Music Genre Classification Based on Deep Learning

Abstract

Human music life can be traced back to ancient times. The music art of human society is rich and colorful, which makes the music classification unable to classify efficiently and accurately. Moreover, the classification has become a daunting task. On this basis, this paper studies the method of deep learning for processing music classification. Not only is the design structure of music signal channel classified, but also all connected neural networks associated with the music are investigated to design an appropriate network model. According to different music sequence measurements, the feature sequence mechanism of music design feedback optimization is also investigated. The type probabilities of different calculated orbits are measured by softmax activation function, and the function value of cross loss is obtained. Finally, an Adam optimization algorithm is used as the optimization algorithm of the proposed network model. Subsequently, an independent adaptive learning planning rate is designed. By adjusting the network parameters, the first- and second-order estimates of the calculated gradient are classified. The experimental outcomes prove that the anticipated method can meritoriously increase the correctness of music classification and is helpful for music channel classification. Moreover, we also observed that the number of neurons in the network has also a significant impact over the training and testing errors.