Environmental Interference Cancellation of Speech with the Radial Basis Function Networks: An Experimental Comparison

Authors: Nima Hatami

Abstract:

In this paper, we use Radial Basis Function Networks (RBFN) for solving the problem of environmental interference cancellation of speech signal. We show that the Second Order Thin- Plate Spline (SOTPS) kernel cancels the interferences effectively. For make comparison, we test our experiments on two conventional most used RBFN kernels: the Gaussian and First order TPS (FOTPS) basis functions. The speech signals used here were taken from the OGI Multi-Language Telephone Speech Corpus database and were corrupted with six type of environmental noise from NOISEX-92 database. Experimental results show that the SOTPS kernel can considerably outperform the Gaussian and FOTPS functions on speech interference cancellation problem.

Keywords: Environmental interference, interference cancellation of speech, Radial Basis Function networks, Gaussian and TPS kernels.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1083903

References:

[1] R.W. Jones, B.L. Olsen, B.R. Mace, "Comparison of convergence characteristics of adaptive IIR and FIR filters for active noise control in a duct", Applied Acoustics, vol. 68, pp. 729-738, 2007.
[2] J. Elliott Stephen, M. Stothers Ian, and A. Nelson Philip, "A Multiple Error LMS Algorithm and Its Application to the Active Control of Sound and Vibration", IEEE Trans. on Acoustics, Speech, and Signal Processing, Vol. ASSP-35, No. 10, pp. 1423- 1434, 1987.
[3] M. Feder, A. V. Oppenheim, and E. Weinstein, "Maximum Likelihood Noise Cancellation Using the EM Algorithm", IEEE Trans. on Acoustics, Speech, and Signal Processing, Vol. ASSP- 37, No. 2, pp. 204-216, February 1989.
[4] C. K. Chen and T. D. Chiueh, "Multilayer Perceptron Neural Networks for Active Noise Cancellation", in Proc. of the IEEE International Symposium on Circuits and Systems (ISCAS), Atlanta, GA, May 1996.
[5] J. S. Lim, "Evaluation of a correlation subtraction method for enhancing speech degraded by additive white noise", IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-26, pp. 471-472, Oct. 1978.
[6] S. F. Boll, "Suppression of acoustic noise in speech using spectral subtraction", IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-27, pp. 113-120, Apr. 1979.
[7] J. S. Lim and A. V. Oppenheim, "Enhancement and bandwidth compression of noisy speech", Proc. IEEE, vol. 67, pp. 1586-1604, Dec. 1979.
[8] J. S. Lim, A. V. Oppenheim, and L. D. Braida, "Evaluation of an adaptive comb filtering method for enhancing speech degraded by white noise addition", IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-26, pp. 354-358, Aug. 1978.
[9] T. W. Parsons, "Separation of speech from interfering of speech by means of harmonic selection", J. Acoust. Soc. Amer., vol. 60, pp.911-918, Oct. 1976.
[10] H. Sameti, H. Sheikhzadeh, L.Deng, and R. L. Brennan, "HMMBased Strategies for Enhancement of Speech Signals Embedded in Nonstationary Noise", IEEE Trans. Speech and Audio Processing, Vol. 6, No. 5, September 1998.
[11] J. Chen, J. Benesty, Y. Huang, "On the optimal linear filtering techniques for noise reduction", Speech Communication, vol.49, pp. 305-316, 2007.
[12] B. Widrow and E. Walach , "Adaptive Inverse Control". S.S. Series. Prentice Hall International, Englewood Cliffs, NJ, 1996.
[13] I. Cha and S. A. Kassam, "Interference cancellation using radial basis function networks". Signal Processing, vol. 47, pp. 247-268, 1995.
[14] Y. Lu, N. Sundararajan and P. Saratchandran, "Performance evaluation of a sequential minimal radial basis function neural network learning algorithm". IEEE Trans. Neural Networks, vol. 9, pp. 308-318, 1998.
[15] S. Haykin, "Neural Networks: A Comprehensive Foundation", Prentice Hall International, 1999.
[16] G. Wahba, http://www.stat.wisc.edu/~wahba/
[17] K. Mike Tao, "A Closer Look at the Radial Basis Function Networks", conference record of the 27th asilomar conference on signals, systems and computers, vol. 1, pp. 401-405, 1993.
[18] C. M. Bishop, "Pattern recognition and machine learning", Springer, 2006.
[19] A. P. Dempster, N. M. Laird and D. B. Rubin, "Maximum Likelihood from Incomplete Data via de EM Algorithm", in Journal of the Royal Statistical Society, B 39(1) 1-38, 1976.
[20] Y. K. Muthusamy, R. A. Cole, and B. T. Oshika, "The OGI multilanguage telephone speech corpus", Proceedings of the International Conference on Spoken Language Proceedings, Banff, Alberta, Canada, pp 895-898, October 1992.
[21] A. Varga and H. J. M. Steeneken, "Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recognition systems", Speech Communication, Vol. 12, No. 3, pp. 247 - 251, 1993.
[22] K. Hornik, "Multilayer feedforward networks are universal approximators". Neural Networks, vol. 2, pp. 359-366, 1989.