Classifying audio music genres using a multilayer sequential model

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 a multilayer sequential model , in: International Conference on Advanced Machine Learning Technologies and Applications, 20th-22nd March 2021, Cairo, Egypt.

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Abstract

In this paper, we discuss applying a neural network model to classify a dataset of type audio. We used a GTZAN dataset that includes different audio music records representing different conventional categories of music genres. Each shares a set of common traditions; these traditions we call it features. We build our proposed Python models using the Anaconda toolkit that has TensorFlow (TF), an open-source deep-learning library. We build a multilayer sequential model to classify the dataset and then try to solve the model’s overfitting issue. Results show that our proposed overfitting solution tends to increase Testing Accuracy equals 60.55% and reduce the Testing Error by 1.1726.

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 12:59
Last Modified: 27 Aug 2021 21:54
URI: http://usir.salford.ac.uk/id/eprint/61007

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