A Novel RLS Based Adaptive Filtering Method for Speech Enhancement

Speech enhancement is a long standing problem with
numerous applications like teleconferencing, VoIP, hearing aids and
speech recognition. The motivation behind this research work is to
obtain a clean speech signal of higher quality by applying the optimal
noise cancellation technique. Real-time adaptive filtering algorithms
seem to be the best candidate among all categories of the speech
enhancement methods. In this paper, we propose a speech
enhancement method based on Recursive Least Squares (RLS)
adaptive filter of speech signals. Experiments were performed on
noisy data which was prepared by adding AWGN, Babble and Pink
noise to clean speech samples at -5dB, 0dB, 5dB and 10dB SNR
levels. We then compare the noise cancellation performance of
proposed RLS algorithm with existing NLMS algorithm in terms of
Mean Squared Error (MSE), Signal to Noise ratio (SNR) and SNR
Loss. Based on the performance evaluation, the proposed RLS
algorithm was found to be a better optimal noise cancellation
technique for speech signals.

• Pogula Rakesh
• T. Kishore Kumar

• Adaptive filter
• Adaptive Noise Canceller
• Mean Squared Error
• Noise reduction
• NLMS
• RLS
• SNR
• SNR Loss.

[1] P. Krishnamoorthy and S.R. Mahadeva Prasanna, “Temporal and
Spectral Processing Methods for Processing of Degraded Speech: A
Review,” IETE Technical Review, Vol. 26, No. 2, March 2009.
[2] Jingdong C., Jacob B. and Arden Huang., “On the optimal linear
filtering techniques for noise reduction,” Speech Communications, Vol.
49, No. 4, April 2007, pp. 305-316.
[3] Jan Vanus and Vitezslav Styskala, “Application of optimal settings of
the LMS adaptive filter for speech signal processing,” Proceedings of
the 2010 International multiconference on Computer Science and
Information Technology, October 2010, pp.767-774.
[4] Paulo S.R. Diniz, Adaptive Filtering: Algorithms and Practical
Implementation, Springer, 2013.
[5] Raj Kumar Thenua and S.K. Agarwal, “Simulation and performance
analysis of adaptive filter in noise cancellation,” International Journal of
Engineering Science and Technology, Vol. 2, No. 9, 2010, pp. 4373-
4378.
[6] Simon Haykin, Adaptive Filter Theory, Prentice- Hall, 2002.
[7] Emmanuel Ifeachor C., Jervis Barrie W., Digital Signal Processing – A
practical approach, Pearson Education, 2004.
[8] M. Turki-Hadj Alouane and M. Jaídane-Saídane, “A new nonstationary
LMS algorithm for tracking Markovian time varying systems,” Signal
Processing, Volume 86, No. 1, January 2006 pp. 50-70.
[9] A.H. Sayed, Fundamentals of Adaptive Filtering, Wiley, 2003. [10] J. H. Husoy and M. S. E. Abadi, “A comparative study of some
simplified RLS type algorithm,” Proceedings of International
Symposium on control, Communications and Signal Processing, March
2004, pp.705-708.
[11] Komal R. Borisagar and Dr. G.R.Kulkarni, “Simulation and
Comparative Analysis of LMS and RLS Algorithms Using Real Time
Speech Input Signal,” Global Journal of Researches in Engineering,
Vol. 10, No. 5, October 2010, pp. 44-47.
[12] Jianfen Maa and Philipos C. Loizou, “SNR loss: A new objective
measure for predicting the intelligibility of noise-suppressed speech,”
Speech Communication, Vol. 53, No. 3, March 2011, pp. 340–354.