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An Experimental Survey on Non-Negative Matrix Factorization for Single Channel Blind Source Separation

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 100 - Number 5
Year of Publication: 2014
Mona Nandakumar M
Edet Bijoy K

Mona Nandakumar M and Edet Bijoy K. Article: An Experimental Survey on Non-Negative Matrix Factorization for Single Channel Blind Source Separation. International Journal of Computer Applications 100(5):1-6, August 2014. Full text available. BibTeX

	author = {Mona Nandakumar M and Edet Bijoy K},
	title = {Article: An Experimental Survey on Non-Negative Matrix Factorization for Single Channel Blind Source Separation},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {100},
	number = {5},
	pages = {1-6},
	month = {August},
	note = {Full text available}


In applications such as speech and audio denoising, music transcription, music and audio based forensics, it is desirable to decompose a single-channel recording into its respective sources, commonly referred to as blind source separation (BSS). One of the techniques used in BSS is non-negative matrix factorization (NMF). In NMF both supervised and unsupervised mode of operations is used. Among them supervised mode outperforms well due to the use of pre-learned basis vectors corresponding to each underlying sources. In this paper NMF algorithms such as Lee Seung algorithms (Regularized Expectation Minimization Maximum Likelihood Algorithm (EMML) and Regularized Image Space Reconstruction Algorithm (ISRA)), Bregman Divergence algorithm (Itakura Saito NMF algorithm (IS-NMF)) and an extension to NMF, by incorporating sparsity, Sparse Non-Negative Matrix Factorization( SNMF) algorithm are used to evaluate the performance of BSS in which supervised mode is used. Here signal to distortion ratio (SDR), signal to interference ratio (SIR) and signal to artifact ratio (SAR) are measured for different speech and/or music mixtures and performance is evaluated for each combination.


  • Menaka Rajapakse and Lnnce Wyse, "NMF vs ICA for Face Recognition", Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis (Proc. ISPAO3), pp 605-610, 2003.
  • F. Cong, Z. Zhang, I. Kalyakin, T. Huttunen-Scott, H. Lyytinen, and T. Ristaniemi, "Non-negative Matrix Factorization Vs. FastICA on Mismatch Negativity of Children", Proceedings of International Joint Conference on Neural Networks, pp 586-600, June 2009.
  • Daniel D. Lee and H. Sebastian Seung, "Algorithms for Nonnegative Matrix Factorization", Neural Inf. Process. Syst, vol. 13, pp 556-562, 2001.
  • Dempster, AP, Laird, NM Rubin, "Maximum Likelihood from Incomplete Data via the EM Algorithm", J. Royal Stat. Soc, vol. 39, 1977.
  • I. S. Dhillon and S. Sra, "Generalized nonnegative matrix approximations with Bregman divergences", Advances in Neural Information Processing Systems, 19, 2005.
  • Patrik O. Hoyer, "Non-negative Matrix Factorization with Sparseness Constraints ", Journal of Machine Learning Research, vol. 5 , pp. 1457-1469, 2004.
  • Mikkel N. Schmidt, "Speech Separation Using Non-negative Features and Sparse Non-negative Matrix Factorization", Elsevier, 2007.
  • Dennis L. Sun and Gautham J. Mysore, "Universal Speech Models for Speaker Independent Single Channel Source Separation", ICASSP, 2013.
  • http://www. telecom. tuc. gr/ nikos/BSS. . Nikos. html(for obtaining speech/music data base)
  • Emmanuel Vincent, Rmi Gribonval, and Cdric Fvotte, "Performance Measurement in Blind Audio Source Separation", IEEE Transactions on Audio, Speech, and Language Processing, vol. 14, no. 4, pp 1462-1469, July 2006.