Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(865.9KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
An Approach to Reduce Noise in Speech Signals Using an Intelligent System: BELBIC
Edet Bijoy K, Musfir Mohammed
Pages - 120 - 129     |    Revised - 01-07-2011     |    Published - 05-08-2011
Volume - 5   Issue - 3    |    Publication Date - July / August 2011  Table of Contents
MORE INFORMATION
KEYWORDS
BELBIC, Spectral Noise, Adaptive Filtering, Fundamental Frequency, Simulink
ABSTRACT
The two widespread concepts of noise reduction algorithms could be observed are spectral noise subtraction and adaptive filtering. They have the disadvantage that there is no parameter to distinguish between the speech and the noise components of same frequency. In this paper, an intelligent controller, BELBIC, based on mammalian limbic Emotional Learning algorithms is used for increasing the speech quality from a noisy environment. Here the learning ability to train the system to recognize and the output thus obtained would be the fundamental frequency of the speech spectrum thus reducing the noise level to minimum. The parameters on which the reduction of noise from the input speech spectrum depends have also been studied. The real time implementations have been done using Simulink and the results of the analysis thus obtained are included in the end.
CITED BY (2)  
1 Gonzalez-Delgado, L., Valencia-Redrovan, D., Robles-Bykbaev, V., Gonzalez-Delgado, N., & Panzner, T. (2014, October). Fuzzy controller for automatic microphone gain control in an autonomous support system for elderly. In e-Health Networking, Applications and Services (Healthcom), 2014 IEEE 16th International Conference on (pp. 77-81). IEEE.
2 Ravi, R., & Mija, S. J. (2014, May). Design of Brain Emotional Learning Based Intelligent Controller (BELBIC) for uncertain systems. In Advanced Communication Control and Computing Technologies (ICACCCT), 2014 International Conference on (pp. 1089-1093). IEEE.
1 Google Scholar 
2 CiteSeerX 
3 Scribd 
4 SlideShare 
5 PdfSR 
1 . Yoma, N. B.; McInnes, F. R.; Jack, M. A. (1997). “Spectral subtraction and mean normalization in the context of weighted matching algorithms.” In Proc. of EUROSPEECH’97, vol. 3, pp. 1411–1414, Rhodes; Greece.
2 . Pfitzinger, H. R. (1998). “The collection of spoken language resources in car environments.” In ICLRE ’98, vol. 2, pp.1097–1100, Granada; Spain.
3 . D. Shahmirzadi, “Computational Modeling Of The Brain Limbic System And Its Application In Control Engineering”, Master dissertation, Texas A&M University, U.S.A. , (2005).
4 . J. Moren, C. Balkenius, "A Computational Model of Emotional Learning in the Amygdala",Cybernetics and Systems, Vol. 32, No. 6, (2000), pp. 611- 636.
5 . J. Moren, “Emotion and Learning – A Computational Model of the Amygdala”, PhD dissertation, Lund University, Lund, Sweden, (2002).
6 . C. Lucas, D. Shahmirzadi, N. Sheikholeslami, "Introducing BELBIC: Brain Emotional Learning Based Intelligent Controller", International Journal of Intelligent Automation and Soft Computing, Vol. 10, NO.1, (2004), pp. 11- 22.
7 . R. Ventura, and C. Pinto Ferreira, "Emotion based control systems", Proc. Of IEEE Int.symp. On Intelligent control/Intelligent systems and semiotics, Cambridge, MA, (1999), pp.64-66.
8 . D. Purves, G. J. Augustine, D. Fitzpatrick, L. C. Katz, A. LaMantia, J. O. McNamara, S. M.Williams, “Neuroscience” , Sinauer Associates, (2001).
9 . E. T. Rolls, “A Theory of Emotion and Consciousness, and Its Application to Understanding the Neural Basis of Emotion.” Cambridge, MA: MIT Press. (1995).
10 . G.S. and Lidd, M.L., “Automatic gain control. Acoustics, Speech, and Signal Processing Kang”, IEEE International Conference on ICASSP. Volume 9,1984, pp. 120 – 123.
Mr. Edet Bijoy K
MES College of Engineering - India
Mr. Musfir Mohammed
MES College of Engineering - India
mohammed.musfir@ieee.org