Classifying audio music genres using CNN and RNN

Khamees, AA ORCID: https://orcid.org/0000-0002-9324-464X, Hejazi, HD ORCID: https://orcid.org/0000-0001-8726-3660, Alshurideh, M ORCID: https://orcid.org/0000-0002-7336-381X and Salloum, SA ORCID: https://orcid.org/0000-0002-6073-3981 2021, Classifying audio music genres using CNN and RNN , in: International Conference on Advanced Machine Learning Technologies and Applications, 20th-22nd March 2021, Cairo, Egypt.

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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.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Advanced Machine Learning Technologies and Applications : proceedings of AMLTA 2021
Publisher: Springer
Series Name: Advances in Intelligent Systems and Computing
ISBN: 9783030697167 (print); 9783030697174 (ebook)
ISSN: 2194-5357
Related URLs:
Depositing User: USIR Admin
Date Deposited: 22 Jun 2021 12:36
Last Modified: 22 Jun 2021 12:59
URI: http://usir.salford.ac.uk/id/eprint/61005

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