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Classification of Moving Vehicles using Multi-Classifier with Time-Domain Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 71 - Number 1 |
| Year of Publication: 2013 |
| Authors: N. Abdul Rahim, Paulraj M P, A. H. Adom |
10.5120/12321-8536
|
N. Abdul Rahim, Paulraj M P, A. H. Adom . Classification of Moving Vehicles using Multi-Classifier with Time-Domain Approach. International Journal of Computer Applications. 71, 1 ( June 2013), 12-17. DOI=10.5120/12321-8536
Abstract
The hearing impaired is afraid of walking along a street and living a life alone. Since, it is difficult for hearing impaired to hear and judge sound information and they often encounter risky situations while they are in outdoors. The sound produced by moving vehicle in outdoor situation cannot be moderated wisely by profoundly hearing impaired community. They also cannot distinguish the type and the distance of any moving vehicle approaching from their behind. In this paper, a simple system that identifies the type and distance of a moving vehicle using artificial neural network has been proposed. The noise emanated from a moving vehicle along the roadside was recorded together with its type and position. Using time-domain approach, simple feature extraction algorithm for extracting the feature from the noise emanated by the moving vehicle has been developed. Simple time-domain features such as energy and zero-crossing rates are applied for getting the important signatures from the sound. The extracted features were associated with the type and zone of the moving vehicle and a multi-classifier system (MCS) based on neural network model has been developed. The developed MCS is tested for its validity.
References
- W. Huadong, M. Siegel, and P. Khosla, "Vehicle sound signature recognition by frequency vector principal component analysis," in Instrumentation and Measurement Technology Conference, 1998. IMTC/98. Conference Proceedings. IEEE, 1998, pp. 429-434 vol. 1.
- H. Maciejewski, J. Mazurkiewicz, K. Skowron, and T. Walkowiak, "Neural Networks for Vehicle Recognition," in Proceeding of the 6th International Conference on Microelectronics for Neural Networks, Evolutionary and Fuzzy Systems, 1997, p. 5.
- A. Averbuch, E. Hulata, V. Zheludev, and I. Kozlov, "A Wavelet Packet Algorithm for Classification and Detection of Moving Vehicles," Multidimensional Systems and Signal Processing, vol. 12, pp. 9-31, 2001.
- A. Averbuch, V. A. Zheludev, N. Rabin, and A. Schclar, "Wavelet-based acoustic detection of moving vehicles," Multidimensional Systems and Signal Processing, vol. 20, pp. 55-80, 2009.
- K. B. Eom, "Analysis of Acoustic Signatures from Moving Vehicles Using Time-Varying Autoregressive Models," Multidimensional Systems and Signal Processing, vol. 10, pp. 357-378, 1999.
- M. E. Munich, "Bayesian Subspace Methods for Acoustic Signature Recognition of Vehicles," in Proceeding of the 12th European Signal Processing Conference, 2004, pp. 1-4.
- L. Bing, A. Dibazar, and T. W. Berger, "Nonlinear Hebbian Learning for noise-independent vehicle sound recognition," in Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on, 2008, pp. 1336-1343.
- E. Alexandre, L. Cuadra, L. Álvarez, M. Rosa-Zurera, and F. López-Ferreras, "Automatic Sound Classification for Improving Speech Intelligibility in Hearing Aids Using a Layered Structure," in Intelligent Data Engineering and Automated Learning – IDEAL 2006, 2006, pp. 306-313.
- E. Alexandre-Cortizo, M. Rosa-Zurera, and F. Lopez-Ferreras, "Application of Fisher Linear Discriminant Analysis to Speech/Music Classification," in Computer as a Tool, 2005. EUROCON 2005. The International Conference on, 2005, pp. 1666-1669.
- S. Sampan, "Neural Fuzzy Techniques in Vehicle Acoustic Signal Classification," in Faculty of the Virginia Polytechnic Institute and State University, 1997, p. 172.
- L. Rabiner and R. Schafer, Theory and Applications of Digital Speech Processing Prentice Hall, 2010.
- L. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms: Wiley-Interscience, 2004.
- R. Romesh and P. Vasile, "Multi-Classifier Systems: Review and a roadmap for developers," Int. J. Hybrid Intell. Syst. , vol. 3, pp. 35-61, 2006.
- N. A. Rahim, M. P. Paulraj, A. H. Adom, and S. Sundararaj, "Moving Vehicle Noise Classification using Backpropagation Algorithm," in 2010 6th International Colloquium on Signal Processing & Its Applications, 2010, p. 6.
- S. N. Sivanandam and M. Paulraj, Introduction to Artificial Neural Networks: VIKAS Publishing House Pvt Ltd, India, 2003.
- N. A. Rahim, M. N. Taib, A. H. Adom, and M. Y. Mashor, "The NARMAX Model for a DC Motor using MLP Neural Network," in Proceeding of the First International Conference 0n MAN-MACHINE SYSTEMS (ICoMMS), 2006, pp. 61-65.
- R. Polikar, "Ensemble based systems in decision making," Circuits and Systems Magazine, IEEE, vol. 6, pp. 21-45, 2006.
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