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Effective audio classification algorithm swarm-based optimization

Bae, Changseok and Wahid, Noorhaniza and Chung, Yuk Ying and Yeh, Wei-Chang and Bergmann, Neil William and Chen, Zhe (2014) Effective audio classification algorithm swarm-based optimization. International Journal of Innovative Computing, Information and Control, 10 (1). pp. 151-167. ISSN 13494198

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The effectiveness and usefulness of large audio databases is greatly dependent on the ability to classify and retrieve audio files based on their properties or content. Automatic classification using machine learning is much more practical than manual classification. In this paper, a new audio classification algorithm using Simplified Swarm Optimization (SSO) based on Particle Swarm Optimization (PSO) is presented. The performance of the new algorithm is compared with two existing state-of-the-art classifiers, PSO and Support Vector Machine (SVM), for an audio dataset being classified into five classes of musical instruments. The experimental results show that the proposed SSO-based classifier has improved classification accuracy (91.7%) when compared with PSO (87.2%) and SVM (88.5%). Additionally, the algorithm is shown to have simpler particle update calculations than PSO, and also requires fewer particles for classification training.

Item Type: Article
Uncontrolled Keywords: Audio classification; swarm-based optimization; classification
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Multimedia
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 13 Aug 2018 03:20
Last Modified: 13 Aug 2018 03:20
URI: http://eprints.uthm.edu.my/id/eprint/9623
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