Call for Paper - August 2022 Edition
IJCA solicits original research papers for the August 2022 Edition. Last date of manuscript submission is July 20, 2022. Read More

Blind Audio Source Separation: State-of-Art

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2015
Abouzid Houda, Chakkor Otman

Abouzid Houda and Chakkor Otman. Article: Blind Audio Source Separation: State-of-Art. International Journal of Computer Applications 130(4):1-6, November 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Abouzid Houda and Chakkor Otman},
	title = {Article: Blind Audio Source Separation: State-of-Art},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {130},
	number = {4},
	pages = {1-6},
	month = {November},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


The word is surrounded by sounds what makes it difficult when it becomes impossible to obtain a desired speech because of the noisy environment. Thus, digital signal processing is a discipline that interest to extract useful information on physical phenomena from measures generally disturbed. Its most well know problem is the blind sources separation which is a specific method that in which several signals have been mixed and the purpose is to recover the original component signals from the mixed signals without any knowledge about the sources.

This work, provides some of many existing algorithms solving the problem of blind source separation the most known in literature and at the end of this article there are some examples applied to real-world audio separation tasks using Matlab.


  1. R.Badeau .‘’separation de source audio, projet et applications musicales (PAM) , master ATIAM‘’. TelecomPerisTech.
  2. A. Ikhlef and D. Le Guennec, "A Simplified Constant Modulus Algorithm for Blind Recovery of MIMO QAM and PSK Signals : A Criterion with Convergence Analysis," EURASIP Journal on Wireless Communications and Networking, vol. 2007, Article ID 90401, 13 pages, 2007. doi :10.1155/2007/90401.
  3. A.Tamaber and S.Mouheb. these.“La séparation aveugle de sources non stationnaires,”. 2012–2013.
  4. S. Makino, T.-W. Lee, and H. Sawada,“Blind Speech Separation”. Springer, 2007.
  5. J.F,Cardoso and A.Souloumiac. “Blind beamforming for non-Gaussian signals”.IEE PROCEEDINGS–F, vol.140,No.6,December 1993.
  6. Y. Li, D. Powers, and J. Peach, “Comparison of blind source separation algorithms,” Adv. Neural Networks Appl., no. C, pp. 18–21, 2000.
  7. K .Velada, I.Yaylati , M.Cabririzo,M.Goryawala and M.Adjaoudi.”Peak detection of somatosensory evoked potentials using an integrated principal component analysis-walsh method”, journal of clinical neurophysiolog,vol.29,number 2,April.2012.
  8. V. Matic and W. Deburchgraeve, “comparison of ICA algorithms for ECG artifact removal from EEG signals. IEEE-EMBS Benelux Chapter Symposium, 2009.
  9. Hyvarinen, A. and Erkki ,O. (1997) . A fast fixed-point Algorithm for Independent Component Analysis. IEEE Neural computation, 9:1483-1492.
  10. Takatani,T.Nishikawa,T Saruwatari,H and Shikano,K .(2003).High-fidelity blind separation of scoustic signals using simo-model-based ICA with information-geometric learning. IWAENC 2003.
  11. Nishikawa, T. Saruwatari, H and Shikano, K .Stable and low –Distortion Algorithm based on overdetermined blind separation for convolutive mixture of speech. Springer-Verlag Berlin Heiderburg.2004.
  12. Project funded by the European Community under the “Information society Technologie”Programme 1998-2002.”Technical report on implementation of linear methods and validation on acoustic sources”.2003.BLISS, IST-1999 14190.
  13. T. Zeman,”BSS-Preprocessing Steps for Separation Improvement”.CTU FEE.Dept of Circuit Theory, May.2000.
  14. O.chakkor, Carlos Garcia Puntonet, Mohammed Essaadi. A Survey of Signal Separation Algorithms. International Journal of Computer Applications, Volume 54 - Number 8, 2012.


Blind source separation (BSS); convolutive mixture; instant linear mixture; independent component analysis; principle component analysis.