Abstract
In this paper, the problem of speech deconvolution is solved. This problem is encountered in limited-bandwidth speech communication systems such as telephone systems. Three solutions are presented for this problem. In the first solution, a Linear Minimum Mean Square Error (LMMSE) approach is used. The necessary assumptions required to reduce the computational complexity of the LMMSE solution are presented. In the second solution, an inverse filter deconvolution approach is presented. Finally, the regularization theory is used to solve this problem. The common thread between all these solutions is that they treat the speech deconvolution problem as an inverse problem considering the speech degradation model. Simulation results reveal the superiority of these solutions for solving the speech deconvolution problem.
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References
Anderws, H. C., & Hunt, B. R. (1977). Digital image restoration. Englewood Cliffs: Prentice-Hall.
Bai, H., & Wan, E. A. (2003). Two-pass quantile based noise spectrum estimation. Center of Spoken Language Understanding, OGI School of Science and Engineering at OHSU, http://cslu.cse.ogi.edu/publications.
Berouti, M., Schwartz, R., & Makhoul, J. (1979). Enhancement of speech corrupted by acoustic noise. In Proc. IEEE int. conf. acoust., speech signal processing (pp. 208–211).
Boll, S. F. (1979). Suppression of acoustic noise in speech using spectral subtraction. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-27, 113–120.
Deller, J. R., Hansen, J. H. L., & Proakis, J. G. (2000). Discrete-time processing of speech signals (2nd edn.). New York: IEEE Press.
El-Khamy, S. E., Hadhoud, M. M., Dessouky, M. I., Salam, B. M., & Abd El-Samie, F. E. (2004). Optimization of image interpolation as an inverse problem using the LMMSE algorithm. In Proc. IEEE MELECON (pp. 247–250).
Ephriam, Y., & Van Trees, H. L. (1993). A signal subspace approach for speech enhancement. In Proc. international conference on acoustic, speech and signal processing, Detroit, MI, USA, May 1993 (Vol. II, pp. 355–358).
Ephriam, Y., & Van Trees, H. L. (1995). A spectrally-based signal subspace approach for speech enhancement. In IEEE ICASSP (pp. 804–807).
Figueriredo, M., & Nowak, R. (2005). A bound optimization approach to wavelet-based image deconvolution. In IEEE intern. conf. on image processing—ICIP’05, Genoa, Italy.
Galatsanos, N. P., & Chin, R. T. (1989). Digital restoration of multichannel images. IEEE Transactions on Acoustics, Speech, and Signal Processing, 37(3), 415–421.
Ghanbari, Y., & Karami, M. (2004). Spectral subtraction in the wavelet domain for speech enhancement. International Journal of Software and Information Technologies, 1, 26–30.
Ghanbari, Y., Karami, M., & Amelifard, B. (2004). Improved multi band spectral subtraction method for speech enhancement. In Proc. 6th IASTED internat. conf. on signal image process, USA (pp. 225–230).
Haykin, S. (1996). Adaptive filter theory. New York: Prentice Hall. ISBN 0-13-322760-X.
Hu, Y., & Loizou, P. (2002). A subspace approach for enhancing speech corrupted by colored noise. In Proc. international conference on acoustics, speech and signal processing, Orlando, FL, USA, May (Vol. I, pp. 573–576).
Jalobeanu, A., Kingsbury, N., & Zerubia, J. (2001). Image deconvolution using hidden Markov tree modeling of complex wavelet packets. In IEEE intern. conf. on image processing—ICIP’01, Thessaloniki, Greece.
Jin, C., & Kubicheck, R. (1996). Vector quantization techniques for output-based objective speech quality. In Proc. ICASSP (pp. 491–494).
Karayiannis, N. B., & Venetsanopouos, A. N. (1990). Regularization theory in image restoration- the stabilizing functional approach. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38(7), 1155–1179.
Leung, W. Y. V., & Bones, P. J. (2001). Statistical interpolation of sampled images. Optical Engineering, 40(4), 547–553.
Lim, J. S., & Oppenheim, A. V. (1978). All-pole modelling of degraded speech. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-26.
Manikandan, S. (2006). Speech enhancement based on wavelet de noising. Academic Open Internet Journal, 17.
O’Shaughnessy, D. (2000). Speech communication: human and machine. The Institute of Electrical and Electronics Engineers, Inc.
O’Shaughnessy, D. (2004). Single ended method for objective speech quality assessment in narrowband telephony applications. In ITU-T (p. 563).
Quackenbush, S. R., Barnwell, T. P., III, & Clements, M. A. (1988). Objective measures of speech quality. Englewood Cliffs: Prentice-Hall.
Rezayee, A., & Gazor, S. (2001). An adaptive KLT approach for speech enhancement. IEEE Transactions on Speech and Audio Processing, 9, 87–95.
Rivaz, P., & Kingsbury, N. (2001). Bayesian image deconvolution and denoising using complex wavelets. In IEEE intern. conf. on image processing—ICIP’01, Thessaloniki, Greece.
Shao, Y., & Chang, C. H. (2005). A versatile speech enhancement system based on perceptual wavelet de noising. In IEEE (pp. 864–867).
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Abd El-Fattah, M.A., Dessouky, M.I., Diab, S.M. et al. Speech deconvolution as an inverse problem. Int J Speech Technol 14, 273–284 (2011). https://doi.org/10.1007/s10772-011-9102-8
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DOI: https://doi.org/10.1007/s10772-011-9102-8