β-Divergence Nonnegative Matrix Factorization on Biomedical Blind Source Separation

A. M. Darsono, C. C. Toh, M. S. Md Saat, A. A. M. Isa, N. A. Manap, M. M. Ibrahim

Abstract


β-divergence has been studied for years, but it is yet to be discovered thoroughly. In this paper, we proposed the nonnegative matrix factorization (NMF) by using β-divergence in blind source separation (BSS) on biomedical field. The proposed idea is basically aimed at the separation of normal heart sound with normal lung sound. Temporal codes and spectral basis were modelled into a separated source, which is applied to the synthesis and real life data using multiplicative update rules. In the experiment, estimated and original source were compared to evaluate the performance of various source separation algorithms within a general framework, where the original sources and the noise that perturbed the mixture were included.

Keywords


Blind Source Separation; Nonnegative Matrix Factorization; β-Divergence; KL Divergence; LSE Divergence;

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References


Hyvarian, J. Karhunen and E. Oja, “Independent component analysis and blind sources separation,” John Wiley and Sons, 2001.

A. Cichocki and S.I.Amari, “Adaptive Blind Signal and Image Processing – Learning Algorithm and Applications,” John Wiley and Sons, 2003.

A. Ozerov and C. Févotte, “Multichannel Nonnegative Matrix Factorization in Convolutive Mixtures for Audio Source Separation,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 18, no. 3, pp. 550-563, March 2010.

I. Biciu, N. Nikolaidis and I. Pitas, “Nonnegative matrix factorization in polynomial feature space”, IEEE Trans. Neural Network, vol. 19, pp. 1090-1100, 2007.

A.M. Darsono, Shakir Saat, N.M. Z. Hashim, A.A.M ISA “Unsupervised Single Channel Source Separation with Nonnegative Matrix Factorization,” ICIT 2015 The 7th International Conference on Information Technology, Amman, Jordan, 2015.

B. Gao, W.L. Woo and S.S. Dlay, “Single Channel Source Separation Using EMD-Subband Variable Regularised Sparse Features,” IEEE Trans. on Audio, Speech, and Language Processing, vol. 19, pp. 961–976, 2011.

W.L. Woo and S.S. Dlay, “Neural network approach to blind signal separation of mono-nonlinearly mixed sources,” IEEE Trans. Circuits and System I, vol. 52, no. 6, pp. 1236-1247, 2005.

J. Zhang, W.L. Woo and S.S. Dlay, “Blind Source Separation of Post-Nonlinear Convolutive Mixture,” IEEE Trans. on Audio, Speech and Language Processing, vol. 15, no. 8, pp. 2311-2330, 2007.

A.M. Darsono, Bin Gao, W.L. Woo, S.S. Dlay, “Nonlinear single channel source separation”, International Symposium on communications systems, networks and digital signal processing (CSNDSP 2010), 2010, pp: 507-511

P. Li, Y. Guan, B. Xu and W. Liu, “Monaural speech separation based on computational auditory scene analysis and objective quality assessment of speech,” IEEE Trans. on Audio, Speech and Language Processing, vol. 14, no. 6, pp. 2014–2023, Nov. 2006.

G. Hu and D.L. Wang, “Monaural speech segregation based on pitch tracking and amplitude modulation,” IEEE Trans. Neural Networks, vol. 15, no. 5, pp. 1135–1150, Sep. 2004.

M.S. Pedersen, D.L. Wang, J. Larsen and U. Kjems, “TwoMicrophone Separation of Speech Mixtures,” IEEE Trans. on Neural Networks, vol. 19, no. 3, pp. 475–492, Mar. 2008.

H. Pasterkamp, R. Fenton, A. Tal and V. Chernick, “Interference of cardiovascular sounds with phonopneumography in children,” Am. Rev. Respir. Dis., vol. 131, no. 1, pp. 61–64, Jan. 1985.

H. Pasterkamp, S. S. Kraman and G. R. Wodicka, “Respiratory sounds: Advances beyond the stethoscope,” Amer. J. Respir. Crit. Care Med., vol. 156, pp. 974-987, 1997.

P. J. Arnott, G. W. Pfeiffer and M. E. Tavel, “Spectral analysis of heart sounds: Relationships between some physical characteristics and frequency spectra of first and second heart sounds in normals and hypertensives,” J Biomed. Eng., vol. 6, no. 2, pp. 121-128, Apr. 1984.

D. D. Lee and H. S. Seung, “Learning the parts of objects with nonnegative matrix factorization,” Nature, vol. 401, pp. 791, 1999.

D. D. Lee and H. S. Seung, “Unsupervised learning by convex and conic coding,” Proceedings of the Conference on Neural Information Processing Systems 9, pp.515–521, 1997.

R. Kompass, “A generalized divergence measure for non-negative matrix factorization”. In Neuroinformatics workshop, Torun, Poland, Sept. 2005.

A.M. Darsono, NZ Haron, Shakir Saat, MM Ibrahim, NA Manap “Blind Audio Source Separation with Sparse Nonnegative Matrix Factorization,” Research Journal of Applied Sciences, Engineering and Technology, vol.7, issue 23, pp. 5015-5020, June 2014.

M. Morup and M. N. Schmidt, “Sparse nonnegative matrix factor 2-D deconvolution,” Technical Report, Technical University of Denmark, Copenhagen, Denmark, 2006.

A.M. Darsono, N. Z. Haron, A. S. Jaafar and M. I. Ahmad, “β-Divergence Two-Dimensional Sparse Nonnegative Matrix Factorization for Audio Source Separation,” IEEE Conference on Wireless Sensors (ICWiSe2013), Dec. 2013


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ISSN: 2180-1843

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