DOIONLINE 
DOIONLINE Publish In |
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN |
 Journal Home Volume Issue |
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Issue |
Volume-6, Issue-2 ( Feb, 2018 ) | |||||||||
Paper Title |
A Signal Separation Method Based on Sparsity Estimation of Source Signals and Non-negative Matrix Factorization | |||||||||
Author Name |
Siyeon Nam, Serin Hong, Deokgyu Yun, Seung Ho Choi | |||||||||
Affilition |
Dept. of Electronic and IT Media Engineering, Seoul National University of Science and Technology Dept. of Electronic Engineering, Seoul National University of Science and Technology | |||||||||
Pages |
53-54 | |||||||||
Abstract |
In order to improve the signal separation performance of the non-negative matrix factorization (NMF), sparse non-negative matrix factorization (Sparse NMF, SNMF) was developed. Existing SNMF algorithm uses arbitrarily determined sparseness without considering the sparseness of individual sound sources. In this paper, we propose a new signal separation method that estimates the sparseness according to the characteristics of a sound source and applies it to SNMF algorithm. Experimental results show that the proposed method has better performance than the existing NMF and SNMF. Keywords - Signal separation, Non-negative matrix factorization, Sparseness, Denoising. | |||||||||
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