Adaptive Noise Reduction Algorithm for Speech Enhancement

Authors: M. Kalamani, S. Valarmathy, M. Krishnamoorthi

Abstract:

In this paper, Least Mean Square (LMS) adaptive noise reduction algorithm is proposed to enhance the speech signal from the noisy speech. In this, the speech signal is enhanced by varying the step size as the function of the input signal. Objective and subjective measures are made under various noises for the proposed and existing algorithms. From the experimental results, it is seen that the proposed LMS adaptive noise reduction algorithm reduces Mean square Error (MSE) and Log Spectral Distance (LSD) as compared to that of the earlier methods under various noise conditions with different input SNR levels. In addition, the proposed algorithm increases the Peak Signal to Noise Ratio (PSNR) and Segmental SNR improvement (ΔSNRseg) values; improves the Mean Opinion Score (MOS) as compared to that of the various existing LMS adaptive noise reduction algorithms. From these experimental results, it is observed that the proposed LMS adaptive noise reduction algorithm reduces the speech distortion and residual noise as compared to that of the existing methods.

Keywords: LMS, speech enhancement, speech quality, residual noise.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1337699

References:

[1] T. Aboulnasr and K. Mayyas, “A robust variable step-size LMS-type algorithm: analysis and simulations”, IEEE Transactions on Signal Processing, vol. 45 no.3, pp.631 – 639, 1997.
[2] I.Almajai and B.Milner, “Visually Derived Wiener Filters for Speech Enhancement”, IEEE Transactions on Audio, Speech and Language Processing, vol. 19 no.6, pp.1642 – 1651, 2011.
[3] F.Asano, S.Hayamizu, T.Yamada and S.Nakamura, “Speech Enhancement Based on the Subspace Method”, IEEE transactions on Speech and Audio Processing, vol. 8 no.5, pp.97-507, 2000.
[4] J.Benesty, H.Rey, L.R.Vega and S.Tressens, “A Nonparametric VSS NLMS Algorithm”, IEEE Signal Processing Letters, vol. 13, no.10, pp. 581-584, 2006.
[5] J.Benesty and Y.Huang, Adaptive Signal Processing: Applications to Real-world Problems. Berlin, Springer, 2003.
[6] N.J.Bershad, “Analysis of the Normalized LMS Algorithm with Gaussian Inputs”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-34, no.4, pp.93-806, 1986.
[7] S.Boll, “Suppression of acoustic noise in speech using spectral subtraction”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-27, no.2, pp.113–120,1979.
[8] H.F.Chi, S.X.Gao, S.D.Soli and A.Alwan, “Band-limited feedback cancellation with a modified Filtered-X LMS algorithm for hearing aids”, Speech Communication, vol. 39, pp.147–161, 2003.
[9] B.Cornelis, M.Moonen and J.Wouters, “Performance Analysis of Multichannel Wiener Filter-Based Noise Reduction in Hearing Aids under Second Order Statistics Estimation Errors”, IEEE Transactions on Audio, Speech and Language Processing, vol. 19, no.5, pp.1368-1381, 2011.
[10] S.C.Douglas, “Fast Implementations of the Filtered-X LMS and LMS Algorithms for Multichannel Active Noise Control”, IEEE Transactions on Speech and Audio Processing, vol. 7, no.4, pp.454-465, 1999.
[11] M.A.A.El-Fattah, M.I.Dessouky, et al., “Speech enhancement with an adaptive Wiener filter”, International Journal on Speech Technology, vol. 17, pp.53–64, 2014.
[12] S.Haykin, Adaptive Filter Theory. Prentice-Hall, 2001.
[13] S.Haykin, Least-Mean-Square Adaptive Filters. Wiley, 2003.
[14] J.Hellgren, “Analysis of Feedback Cancellation in Hearing Aids with Filtered-X LMS and the Direct Method of Closed Loop Identification”, IEEE Transactions on Speech and Audio Processing, vol. 10, no.2, pp.119-131, 2002.
[15] B.Huang, Y.Xiao, J.Sun and G.Wei, “A Variable Step-Size Fx-LMS Algorithm for Narrowband Active Noise Control”, IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no.2, pp.301-312, 2013.
[16] H.C.Huang and J.Lee, “A New Variable Step-Size NLMS Algorithm and Its Performance Analysis”, IEEE Transactions on Signal Processing, vol. 60, no.4, pp.2055-2060, 2012.
[17] Y.Lu and P.C.Loizou, “A geometric approach to Spectral subtraction”, Speech Communication, vol. 50, pp.453-466, 2008.
[18] J.R.Mohammed, M.S.Shafi, S.Imtiaz, R.I.Ansari and M.Khan, “An Efficient Adaptive Noise Cancellation Scheme Using ALE and NLMS Filters”, International Journal of Electrical and Computer Engineering, vol. 2, no.3, pp. 325-332, 2012.
[19] B.K.Mohanty and P.K.Meher, “A High Performance Energy-Efficient Architecture for FIR Adaptive Filter Based on New Distributed Arithmetic Formulation of Block LMS Algorithm”, IEEE Transactions on Signal Processing, vol. 61, no.4, pp.921-932, 2013.
[20] R.Serizel, M.Moonen, J.Wouters and S.H.Jensen, “A Zone-of-Quiet Based Approach to Integrated Active Noise Control and Noise Reduction for Speech Enhancement in Hearing Aids”, IEEE Transactions on Audio, Speech and Language Processing, vol. 20, no.6, pp.1685-1697, 2012.
[21] B.L.Sim, Y.C.Tong, J.Chang and C.T.Tan, “A parametric formulation of the generalized spectral subtraction method”, IEEE Transactions on Speech and Audio Processing, vol. 6, no.4, pp.328–337, 1998.