NMF2D-based source separation using extreme learning machine

Wu, Di, Woo, Wai Lok and Dlay, Satnam (2015) NMF2D-based source separation using extreme learning machine. In: 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP). IEEE. ISBN 978-1-78561-136-0

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Abstract

In this paper, we study Non-negative Matrix Two-Dimensional Factorization (NMF2D) based Single Channel Source Separation (SCSS) using a newly proposed algorithm named Extreme Learning Machine (ELM). Compared with other machine learning algorithms such as Support Vector Machines and Neural Networks, ELM can provide better generalization performance and a much faster learning speed. Unlike conventional researches that concentrate on generating masks for each source, we use ELM to classify estimated sources separated by NMF2D algorithm. We also explore Deep ELM which means more than one hidden layers to improve the performance. While training Deep ELM, a method named layer by layer pre-training is used, but unlike Deep Belief Networks (DNNs) that need to fine-tune the whole network at the end, Deep ELM can be used without iteration fine-tuning. The experiment results show that the performance of proposed method is improved not only in training and testing speed, but also in the quality of separated signal compared with using DNNs and NMF2D.

Item Type:	Book Section
Uncontrolled Keywords:	Nonnegative Matrix Two-Dimensional Factorizations, Extreme Learning Machine, Single channel source separation
Subjects:	H600 Electronic and Electrical Engineering
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	10 Apr 2019 11:33
Last Modified:	10 Oct 2019 20:15
URI:	http://nrl.northumbria.ac.uk/id/eprint/38891

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