Classifying audio music genres using a multilayer sequential model

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

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.

Citation

Khamees, A., Hejazi, H., Alshurideh, M., & Salloum, S. Classifying audio music genres using a multilayer sequential model. Advances in Intelligent Systems and Computing, 301-314. https://doi.org/10.1007/978-3-030-69717-4_30

• 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: 301-314
• Series Title: Advances in Intelligent Systems and Computing
• Series Number: 1339
• Book Title: Advanced Machine Learning Technologies and Applications : proceedings of AMLTA 2021
• ISBN: 9783030697167-(print);-9783030697174-(ebook)
• DOI: https://doi.org/10.1007/978-3-030-69717-4_30
• Publisher URL: https://doi.org/10.1007/978-3-030-69717-4_30
• Related Public URLs: https://doi.org/10.1007/978-3-030-69717-4
• Additional Information: Event Type : Conference