Performance Analysis Of Regularized Adaptive Algorithms For Noise Suppression In Speech Signals

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2017 by IJETT Journal
Volume-44 Number-1
Year of Publication : 2017
Authors : G.Amjad Khan, Dr.K.E.Sreenivasa Murthy


G.Amjad Khan, Dr.K.E.Sreenivasa Murthy "Performance Analysis Of Regularized Adaptive Algorithms For Noise Suppression In Speech Signals", International Journal of Engineering Trends and Technology (IJETT), V44(1),28-31 February 2017. ISSN:2231-5381. published by seventh sense research group

In this Paper, Regularized NLMS algorithm is proposed to enhance the speech signal from the noisy speech. Speech enhancement is a long standing problem with numerous applications like teleconferencing, VoIP, hearing aids and speech recognition degradations 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. Experiments were performed on noisy data which was prepared by adding AWGN, We then compare the noise cancellation performance of proposed Regularized NLMS algorithm with existing Spectral Subtraction method and NLMS algorithm in terms of Mean Squared Error (MSE), Signal to Noise ratio (SNR). Based on the performance evaluation, the proposed Regularized NLMS algorithm was found to be a better optimal noise cancellation technique in speech enhancement applications.


[1] BOLL S F. Suppression of acoustic noise in speech using spectral subtraction[J]. IEEE Trans. Acoustics, Speech, Signal Processing, 1979, 27(2):113-120.
[2] Ephraim Y, Malah D. Speech enhancement using a minimum mean square error short time spectral amplitude estimator. IEEE Transactions on Acoustics, Speech, Signal Processing, 1984, 32(6): 1109-1121.
[3] N. Wiener, The Extrapolation, Interpolation, and Smoothing of Stationary Time Series With Engineering Applications. New York: Wiley, 1949.
[4] P. C. Hansen and S. H. Jensen, “FIR filter representations of Reduced rank noise reduction,” IEEE Trans. Signal Process., vol. 46, no.6, pp.1737--1741, Jun. 1998.
[5] S. Doclo and M. Moonen, “On the output SNR of the speech-distortion weighted multichannel Wiener filter,” IEEE Signal Process. Lett., vol.12, no. 12, pp. 809--811, Dec. 2005.
[6] Y. Ephraim and H. V. Trees, “A signal subspace approach for speech enhancement,” IEEE Trans. Speech Audio Process., vol. 3, no. 4, pp.251–266, Jul. 1995.
[7] D.L. Wang and J.S. Lim, “The unimportance of phase in speech enhancements”, IEEE Trans. Acoust., Speech and Signal Process., Vol.30, pp. 679-681, Aug. 1982.
[8] AllamMousa, MarwaQuados, Sherin Bader, "Adaptive Noise Cancellation Algorithms Sensitivity to Parameters", IEEE Conference Publications on ICMCS (Multimedia And Computing Systems) pp. 1-5, 2011.
[9] Md Zia Ur Rahman," Non Stationary Noise Cancellation in Speech Signals Using Efficient Variable Step Size Higher order Filter", IJRRC, vol.2,No.2, pp. 414- 422, April 2011.
[10] GeorgiIliev and Nikola Kasabov, "Adaptive Filtering with Averaging in Noise Cancellation for Voice and Speech Recognition", Department of Information Science, University of Otago.
[14] N.Sonbolestan, S.A.hadei,"A Fast Affine Projection Algorith Based on Matching Pursuit in Adaptive noise Cancellation Speech Enhancement "IEEE Conference Publications on ISMS 27-29 jan 2010.
[11] Thamer M. Jamel, HaiderAbd Al-Latifmohamed, "Performance Improvements of Adaptive noise Canceller using New Adajusted Step Size LMS Algorithm", 3rd IEEE Conference on ICSPS august 2728 2011.
[12] E.CIfeachor and B.W.Jervis, Digital Signal Processing, A Practical Approach, Prentice Hall, 2002.
[13] Raymond H. Kwong and Edward W. Johnston, “A Variable Step Size LMS Algorithm”, IEEE Trans. On Signal Processing vol. 40, No.7, pp. 1633-1642, July, 1992.
[14] Shoji Makino, Member, IEEE, Yutaka Kaneda, Member, IEEE and Nobuo Koizumi, “Exponentially weighted step size NLMS adaptive filter based on the statistics of a room impulse response”, IEEE Trans. on speech and audio Processing, vol. 1, No.1, pp.101-108, Jan 1993.leads to sub band adaptive algorithm.
[15] M.Kalamani,S.Valarmurthy,M.Krishnamoorthi , "Modified Noise Reduction Algorithm for Speech Enhancement", Applied Mathematical Sciences,HIKARI Ltd,vol.8,no.89,4447-4452, 2014.
[16] Ekaterina Verteletskaya, Boris Simak ," Noise Reduction Based on Modified Spectral Subtraction Method ",IAENG International Journal of Computer Science, IJCS_38_1_10, 38:1, submitted February, 2011.
[17] Pogula Rakesh, T. Kishore Kumar,” A Novel RLS Based Adaptive Filtering Method for Speech Enhancement”, World Academy of Science, Engineering and Technology International Journal of Electrical, Computer, Energetic, Electronic and Communication EngineeringVol:9, No:2, 2015.
[18] H.C.Huang and J.Lee, “A New Variable Step-Size NLMS Algorithmand Its Performance Analysis”, IEEE Transactions on SignalProcessing, vol. 60, no.4, pp.2055-2060, 2012.
[19] Y.Lu and P.C.Loizou, “A geometric approach to Spectral subtraction”,Speech Communication, vol. 50, pp.453-466, 2008.
[20] J.R.Mohammed, M.S.Shafi, S.Imtiaz, R.I.Ansari and M.Khan, “AnEfficient Adaptive Noise Cancellation Scheme Using ALE and NLMS Filters”, International Journal of Electrical and Computer Engineering,vol. 2, no.3, pp. 325-332, 2012.
[21] B.K.Mohanty and P.K.Meher, “A High Performance Energy-EfficientArchitecture for FIR Adaptive Filter Based on New DistributedArithmetic Formulation of Block LMS Algorithm”, IEEE Transactionson Signal Processing, vol. 61, no.4, pp.921-932, 2013

spectral Subtraction, Normalised NLMS, Regularised NLMS, SNR, MSE.