IJMLC 2012 Vol.2(1): 24-29 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.84

A Bayesian Approach for Single Channel Speech Separation

Sonay Kammi and Mohammad Reza Karami

Abstract—This paper addresses the problem of single channel speech separation to extract and enhance the desired speech signals from mixed speech signals. We propose a new speech separation algorithm by utilizing Bayesian approach for the case in which, underlying sources are mixed at different levels of energies. This situation is not considered in many single channel speech separation methods. To validate the effectiveness of our proposed method, it is compared with a state-of-the art method which is a gain adapted Maximum Likelihood estimator. Through the experiments, we show that our proposed method outperforms the compared method.

Index Terms—Single channel speech separation, bayesian approach, gain estimation.

Sonay Kammi and Mohammad Reza Karami are with the faculty of Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran (e-mail: sonaykammi@yahoo.com; mkarami@nit.ac.ir).


Cite: Sonay Kammi and Mohammad Reza Karami, "A Bayesian Approach for Single Channel Speech Separation," International Journal of Machine Learning and Computing vol. 2, no. 1, pp. 24-29, 2012.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net