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A Novel Approach of Speech Enhancement by Implementing Cellular Automata Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 145 - Number 8 |
| Year of Publication: 2016 |
| Authors: Mahesh B. Adhav, Jagdish D. Kene |
10.5120/ijca2016910709
|
Mahesh B. Adhav, Jagdish D. Kene . A Novel Approach of Speech Enhancement by Implementing Cellular Automata Algorithm. International Journal of Computer Applications. 145, 8 ( Jul 2016), 1-4. DOI=10.5120/ijca2016910709
Abstract
The main purpose of speech enhancement technique is to eliminate the background noise from speech signal to improve the quality of the speech signal. Speech signal is often corrupted by additive background noise like train noise, market noise etc. In such noisy environment listening at the end user is very difficult. This paper presents speech enhancement method using Cellular Automata (CA) algorithm. Based on Peak Signal to Noise Ratio and Mean Square Error criterion the system evaluate the amount of surrounding noise present in speech. The proposed algorithm helps to improve the quality of speech signal at the listener end.
References
- Burks, A. W.: Von Neumann’s self-reproducing automata. In Burks (1970a).
- L. Du, R. Xu, F. Xu, D. Wang, H. Chen, “Research on Key Parameters of Speech Denoising Algorithm Based on Wavelet Packet Transform,” Computer Science and Information Technology (ICCSIT), 3rd IEEE International Conference on, Vol. 6, pp. 551 – 556, July 2010.
- W. Kim, S. Kang, and H. ko “Spectral subtraction Based on phonetic dependency and masking effects” IEEE Proc. vision image signal process, Vol. 14, no.5, pp423-427,2000.
- S. Kamath and P. Loizou, “A multi-band spectral subtraction method for enhancing speech corrupted by colored noise,” IEEE international conference on acoustics speech and signal processing, Vol.4, pp. 4164–4167, May 2002.
- M. B. Adhav, J. D. Kene, “Effective Study Of Feature Extraction Methods For Speaker Identification,” International Journal of Science, Engineering and Technology Research (IJSETR), Vol.4, no.12, pp. 4384-4387, December 2015.
- K. Yamashita, S. Ogata, and T. Shimamura, "Improved spectral subtraction utilizing iterative processing," Electronics and Communications, Japan, vol. 90, no. 4, pp. 39-51, 2007.
- F. Ykhlef, H. Ykhlef, “A smoothed Minimum Mean-Square Error Log-Spectral Amplitude estimator for speech enhancement,” Multimedia Computing and Systems (ICMCS), 2014 International Conference on, pp. 246-249, Apr. 2014.
- T. Gerkmann and R. C. Hendriks, “Unbiased MMSE-based noise power estimation with low complexity and low tracking delay,” IEEE Trans Audio, Speech, Lang. Process., vol. 20, no. 4, pp. 1383–1393, May 2012.
- R. Santhoshkumar, Dr. B. Kirubagari, “Speech Enhancement Using Super Soft Thresholding in Wavelet Domain,” International Journal of Advanced Research in Computer Science and Software Engineering, Vol.5, no.2, pp. 315-320, February 2015.
- S. Wang , Y. Shi, “An Improved speech denoising Algorithm Based on Adaptive Least Mean Square ,” International Conference on Industrial and Information Systems, pp. 293 – 296, April 2009.
- R. Martin, “Speech Enhancement Based on Minimum Mean-Square Error Estimation and Supergaussian Priors ,” IEEE Trans. on Speech And Audio Processing, Vol. 13, No. 5, pp. 845-856, September 2005.
- H. Aso, N. Honda, “Dynamical characteristics of linear cellular automata, ” Journal of Computer and System Sciences, Vol.30, no.3, Pp. 291-317, June 1985.
- Cole, S. N., “Real-time computation by n-dimensional iterative arrays of finite-state machines,” IEEE Trans. on Computers, Vol.18, no.4, pp.349–365, April 1969.
- N. Krishnamurthy, J. H. L. Hansen, “Babble Noise: Modeling, Analysis, and Applications,” IEEE Trans. On Audio, Speech, And Language Processing, Vol. 17, No. 7, pp. 1394-1407, September 2009.
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