Abstract: With the rapid development of electronic music industry, how to establish a set of electronic music genre automatic classification technology has also become an urgent problem. This paper utilized neural network (NN) technology to classify electronic music genres. The basic idea of the research was to establish a deep neural network (DNN) based classification model to analyze the audio signal processing and classification feature extraction of electronic music. In this paper, 2700 different types of electronic music were selected as experimental data from the publicly available dataset of W website, and substituted into the convolutional neural network (CNN) model, PMG-Net electronic music genre classification model and traditional classification model for comparison. The results showed that the PMG-Net model had the best classification performance and the highest recognition accuracy. The classification error of PMG-Net electronic music genre classification model in each round of training was smaller than the other two classification models, and the fluctuation was small. The speed of music signal processing in each round and the feature extraction of audio samples of PMG-Net electronic music genre classification model were faster than the traditional classification model and CNN model. It can be seen that using the PMG-Net electronic music genre classification model customized based on DNN for automatic classification of electronic music genres has a better classification effect, and can achieve the goal of efficiently completing the classification in massive data.

Yuemei Tang, “PMG-Net: Electronic Music Genre Classification using Deep Neural Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140877

@article{Tang2023,
title = {PMG-Net: Electronic Music Genre Classification using Deep Neural Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140877},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140877},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Yuemei Tang}
}