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.
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 |
Actions (login required)
 |
Edit record (repository staff only) |
Tools
Tools