Music Genre Classification Based on Deep Learning

被引:0
作者
Zhang, Wenlong [1 ]
机构
[1] Yantai Univ, Sch Mus & Dance, Yantai 264005, Peoples R China
关键词
RECOGNITION; NETWORKS; MODEL;
D O I
10.1155/2022/2376888
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:11
相关论文
共 30 条
[1]   Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction [J].
Ali, Ahmad ;
Zhu, Yanmin ;
Zakarya, Muhammad .
NEURAL NETWORKS, 2022, 145 :233-247
[2]   Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks [J].
Ali, Ahmad ;
Zhu, Yanmin ;
Zakarya, Muhammad .
INFORMATION SCIENCES, 2021, 577 :852-870
[3]   A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing [J].
Ali, Ahmad ;
Zhu, Yanmin ;
Zakarya, Muhammad .
MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (20) :31401-31433
[4]   Text detection and recognition in images and video frames [J].
Chen, DT ;
Odobez, JM ;
Bourlard, H .
PATTERN RECOGNITION, 2004, 37 (03) :595-608
[5]   In-Vehicle Infotainment Systems: Using Bayesian Networks to Model Cognitive Selection of Music Genres [J].
Dimitrakopoulos, George J. ;
Panagiotopoulos, Ilias E. .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 22 (11) :6900-6909
[6]   A survey on deep learning and its applications [J].
Dong, Shi ;
Wang, Ping ;
Abbas, Khushnood .
COMPUTER SCIENCE REVIEW, 2021, 40
[7]   Music genre classification and music recommendation by using deep learning [J].
Elbir, A. ;
Aydin, N. .
ELECTRONICS LETTERS, 2020, 56 (12) :627-629
[8]   Speech signal enhancement in cocktail party scenarios by deep learning based virtual sensing of head-mounted microphones [J].
Fischer, Tim ;
Caversaccio, Marco ;
Wimmer, Wilhelm .
HEARING RESEARCH, 2021, 408
[9]  
Gannot S., 2019, IEEE Signal Processing Magazine, V36, P136, DOI [10.1109/msp.2018.2888634, DOI 10.1109/MSP.2018.2888634]
[10]  
Guo W., 2020, IEEE J OCEANIC ENG, V43, P1