The implementation of a proposed deep-learning algorithm to classify music genres

Abstract

Abstract To improve the classification effect of music genres in the digital music era, the article employs deep-learning algorithms to improve the performance of the classification of music genres. An auxiliary (estimated) model is constructed to estimate the amount of unmeasured data in the dual-rate system to enhance the recognition effect of music features. Moreover, a dual-rate output error model to identify such impacts is proposed to eliminate the impact of corrupt data caused by the estimation, which eventually leads to the further improvement of the proposed model called dual-rate multi-innovation forgetting gradient algorithm based on the auxiliary model. In addition, the article employs linear time-varying forgetting factors to improve the stability of the system, advances the recognition effect of music features through enhancement processing, and combines a deep-learning algorithm to construct a classification system of music genres. The result shows that the classification of the music genre system based on a deep-learning algorithm has a good music genre classification effect.