Fuzzy Voice Coding with Significant Impulses Modeling and Redundant Waveform Recycling


This paper proposes a voice codification based on two algorithms that make the wave form codification in time domain. The first uses the significant impulse model (SIM), which has as a goal to operate as an endpoint detector and as a dawn sampling, through the detection and selection of the significant valleys and crests; the second algorithm, is a redundant wave-form recycler (RWR) that uses an architecture based on fuzzy logic with an accumulative memory. The fuzzy algorithm obtains the similitude grade between the redundant wave forms, this with the objective of save into an knowledge base the patterns, based on the no supervised learning and when there are into memory, automatically there will be used to identified their arrive respect to the input signal, substituting the input block by the correspondent pattern into memory. This decoding process is using the SIM interpolation with a memory in accordance to the RWR.

Share and Cite:

I. Rosas Arriaga, J. C. García Infante and J. Carlos Sánchez García, "Fuzzy Voice Coding with Significant Impulses Modeling and Redundant Waveform Recycling," Communications and Network, Vol. 5 No. 1, 2013, pp. 50-56. doi: 10.4236/cn.2013.51005.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] A. Gersho and V. Cuperman, “Vector Quantization: A pattern-Matching Technique for Speech Coding,” IEEE Communications Magazine, Vol. 21, No. 9, 1983, pp. 15-21.
[2] J. Makhoul, S. Roucos and H. Gish, “Vector Quantization in Speech Coding,” Proceedings of the IEEE, Vol. 73, No. 11, 1985, pp. 1551-1588.
[3] A. M. Kondoz, “Digital Speech Coding for Low Bit Rate Communication Systems,” 2nd Edition, John Wiley & Sons, Chichester, 2004. doi:10.1002/0470870109
[4] A. Vasuki and P. Vanathi, “A Review of Vector Quantization Techniques,” IEEE Potentials, Vol. 25, No. 4, 2006, pp. 39-47.
[5] N. B. Karayiannis, “A Methodology for Constructing Fuzzy Algorithms for Learning Vector Quantization,” IEEE Transactions on Neural Networks, Vol. 8, No. 3, 1977, pp. 505-518.
[6] C. E. Pedreira, “Learning Vector Quantization with Training Data Selections,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 1, 2006, pp. 157-162.
[7] C. W. Tsai, C.Y. Lee, M. C. Chiang and C. S. Yang, “A Fast VQ Codebook Generation Algorithm via Pattern Reduction,” Pattern Recognition Letters, Vol. 30, No. 7, 2009, pp. 653-660.
[8] G. E. Tsekouras, D. Darzentas, I. Drakoulaki and A. D. Niros, “Fast Fuzzy Vector Quantization,” IEEE International Conference on Fuzzy Systems, Barcelona, 18-23 July 2010, pp. 1-8. doi:10.1109/FUZZY.2010.5584446
[9] P. Kroon, E. Deprettere, R. Sluyter, AT&T Bell Laboratories, “Regular-Pulse Excitation—A Novel Approach to Effective and Efficient Multipulse Coding of Speech,” IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. 34, No. 5, 1986, pp. 1054-1063.
[10] I. McLoughlin, “Applied Speech and Audio Processing with Matlab? Examples,” Cambringe University Press, Cambringe, 2009. doi:10.1017/CBO9780511609640
[11] I. J. Hualde, “The Sounds of Spanish,” Cambrindge University Press, Cambringe, 2005.
[12] E. Mamdani, “Applications of Fuzzy Algorithms for Control of Simple Dynamic Plant,” Proceedings of the Institution of Electrical Engineers, Vol. 121, No. 12, 1974, pp.1585-1588.
[13] L. Zadeh, “Fuzzy Sets, Information and Control,” Information and Control, Vol. 8, No. 3, 1965, pp. 338-353. doi:10.1016/S0019-9958(65)90241-X
[14] T. Takagi and M. Sugeno, “Fuzzy Identification of Systems and Its Applications to Modelling and Control,” IEEE Transactions and Systems, Man, and Cybernetics, Vol. 15, No. 1, 1986, pp. 116-132. doi:10.1109/TSMC.1985.6313399
[15] K. M. Passino, “Fuzzy Control,” Addison Wesley, Boston, 1998.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.