AUTHOR=Xu Zhongkui 

TITLE=Construction of Intelligent Recognition and Learning Education Platform of National Music Genre Under Deep Learning

JOURNAL=Frontiers in Psychology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.843427

DOI=10.3389/fpsyg.2022.843427

ISSN=1664-1078

ABSTRACT=In order to study the application of deep learning (DL) method in music genre recognition, this paper introduces the music feature extraction method and the deep belief network (DBN) in DL, and proposes the feature parameter extraction and recognition classification method of ethnic music genre based on DBN with five kinds of ethnic musical instruments as the experimental objects. A national musical instrument recognition and classification network structure based on DBN is proposed. On this basis, a music library classification retrieval learning platform is established and tested. The results show that when DBN only contains one hidden layer, and the number of neural nodes in the hidden layer is 117, the basic convergence accuracy is about 98 %. The first hidden layer has the greatest impact on the prediction results. When the input sample feature size is one third of the number of nodes in the first hidden layer, the network performance is basically convergent. DBN is the best way for softmax to identify and classify national musical instruments, and the accuracy rate is 99.2 %. Therefore, the proposed DL algorithm has good performance in identifying music genres.