Classifying audio music genres using CNN and RNN

Khamees, AA; Hejazi, HD; Alshurideh, M; Salloum, SA

Abstract

This paper discusses applying different types of neural networks to classify a dataset of type audio. We used a GTZAN dataset that includes various audio music records representing different conventional categories of music genres. Each shares a set of common traditions; these traditions we call features. We build our proposed Python models using the Anaconda toolkit with TensorFlow (TF) an open-source deep-learning library. In our previous research, we build a multilayer sequential model to classify the dataset and then solve the overfitting issue in that model. In this paper, we build a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) with Long Short Time Memory (LSTM). Finally, we compared the results to know the capabilities and limitations of Deep Learning (DL). CNN outperformed the other models in terms of training and test accuracy, having 83.74% and 74%, respectively.

Citation

Khamees, A., Hejazi, H., Alshurideh, M., & Salloum, S. Classifying audio music genres using CNN and RNN. Advances in Intelligent Systems and Computing, 315-323. https://doi.org/10.1007/978-3-030-69717-4_31

• Journal Article Type: Conference Paper
• Conference Name: International Conference on Advanced Machine Learning Technologies and Applications
• Conference Location: Cairo, Egypt
• End Date: Mar 22, 2021
• Online Publication Date: Mar 5, 2021
• Deposit Date: Jun 22, 2021
• Journal: Advanced Machine Learning Technologies and Applications : proceedings of AMLTA 2021
• Electronic ISSN: 2194-5365
• Publisher: Springer
• Pages: 315-323
• Series Title: Advances in Intelligent Systems and Computing
• Series Number: 1339
• Book Title: Advanced Machine Learning Technologies and Applications
• ISBN: 9783030697167-(print);-9783030697174-(ebook)
• DOI: https://doi.org/10.1007/978-3-030-69717-4_31
• Publisher URL: https://doi.org/10.1007/978-3-030-69717-4_31
• Related Public URLs: https://doi.org/10.1007/978-3-030-69717-4
• Additional Information: Event Type : Conference