Robust Feature Sets for Implementation of Classification Machines
Keywords:
Classifying Machine, Discrete sine transform, Statistical estimators, Hidden Markov Models, H-statistic, F-statisticAbstract
Classification Machines have evolved over a lot during recent times, in the field of engineering and sciences. Various classification schemes have been developed, taking into account, the aspect that can be optimized to give maximum system performance. The feature set in a classifier system is very significant, since it determines the efficiency and performance of the machine. Three powerful feature sets possessing robust classifying capabilities are discussed in this paper. Cepstral coefficient analysis based Kruskal-Wallis H statistic, F-test statistic and Discrete Sine Transform based features are found to be very effective for detection and classification of signals. Simulation results for typical data set are also presented in this paper. Statistical estimators, Neural Network and Hidden Markov Model based classifiers, along with various deep learning algorithms can be incorporated along with these feature sets to implement an efficient classifying machine. Typical results based on these feature sets are also presented for different signal sources.
References
Binesh, T, Supriya M H and Saseendran Pillai, P R, “A Non-Parametric Estimation based Underwater Target Classifier”, Signal processing: an International Journal, vol 5, no.4, pp.156-164, 2011.
Prakash, C. and Gangashetty, S.V., "Fourier-Bessel cepstral coefficients for robust speech recognition," in Proc. International Conference on Signal Processing and Communications (SPCOM), 2012, vol., no., pp.1,5, 22-25 July 2012.
Milner, B. and Darch, J., "Robust Acoustic Speech Feature Prediction From Noisy Mel-Frequency Cepstral Coefficients," , IEEE Transactions on Audio, Speech, and Language Processing, vol.19, no.2, pp.338,347, Feb. 2011.
Kumar, K., Chanwoo Kim and Stern, R.M., "Delta-spectral cepstral coefficients for robust speech recognition," in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011 , vol., no., pp.4784,4787, 22-27 May 2011
Siew Wen Chin., Kah Phooi Seng., Ang, Li-Minn., and King Hann Lim, "Improved voice activity detection for speech recognition system," in Proc. International Computer Symposium (ICS), 2010 , vol., no., pp.518,523, 16-18 Dec. 2010.
Valero, X. and Alias, F., "Gammatone Cepstral Coefficients: Biologically Inspired Features for Non-Speech Audio Classification," IEEE Transactions on Multimedia, vol.14, no.6, pp.1684,1689, Dec. 2012.
Zhang He., and Wan Lei; Sun Yushan, "A New Approach to Underwater Target Recognition," in Proc. 2nd International Congress on Image and Signal Processing, 2009. CISP '09, vol., no., pp.1,5, 17-19 Oct. 2009.
Cartmill, J., Azimi-Sadjadi, M.R., and Wachowski, N., "Buried Underwater Object Classification Using a Collaborative Multi-Aspect Classifier," in Proc. International Joint Conference on Neural Networks, IJCNN 2007 , vol., no., pp.1807,1812, 12-17 Aug. 2007.
Bender, A., Williams, S.B., and Pizarro, O., "Classification with probabilistic targets," in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2012, vol., no., pp.1780,1786, 7-12 Oct. 2012.
Xueyao Li, Hua Xie and Bailing Cheng; , "Noisy Speech Enhancement Based on Discrete Sine Transform," in Proc. Computer and Computational Sciences, IMSCCS 2006, vol.1., no., pp.199-202, 20-24.
Binesh,T., Supriya,M.H., and P.R. Saseendran Pillai., “Discrete Sine Transform Based HMM Underwater Signal Classifier,” in Proc. International Symposium on Ocean Electronics, SYMPOL 2011, vol., no. pp.152-155, 16-18.
Liying Ma, and Khorasani, K., "Constructive feedforward neural networks using Hermite polynomial activation functions," IEEE Transactions on Neural Networks, vol.16, no.4, pp.821-833, July 2005.
Bershad, N.J., Celka, P., McLaughlin, S., "Analysis of stochastic gradient identification of Wiener-Hammerstein systems for nonlinearities with Hermite polynomial expansions," IEEE Transactions on Signal Processing , vol.49, no.5, pp.1060,1072, May 2001.
Pawlak Miroslaw, “Nonparametric Identification of a Particular Nonlinear Time Series System”, in Proc. 29th IEEE Conference on Decision and Control, 1990, vol., no., pp. 216-217.
Rabiner, L.R. , "A tutorial on hidden Markov models and selected applications in speech recognition," Proceedings of the IEEE , vol.77, no.2, pp.257-286, Feb 1989.
Sun, Y. and Tong, L. , "A lower ordered HMM approach to blind sequence estimation," in Proc. Military Communications Conference, 1998. MILCOM 1998. IEEE , vol.3, no., pp.847-851, 18-21.
Donghui Li, Azimi-Sadjadi M.R. and Robinson, M, "Comparison of different classification algorithms for underwater target discrimination,", IEEE Transactions on Neural Networks, vol.15, no.1, pp.189-194, Jan. 2004.
Baum, L.E., Petrie, T., Soules, G., and Weiss, N., “A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains,” Annals of Mathematical Stat., vol 41, no.1, pp. 164-171., 1970.
Greblicki, W., "Nonparametric identification of Wiener systems by orthogonal series," IEEE Transactions on Automatic Control, , vol.39, no.10, pp.2077,2086, Oct 1994.
Selesnick, I.W. and Burrus, C.S.; , "Generalized digital Butterworth filter design," in Proc. Acoustics, Speech, and Signal Processing ICASSP 1996, vol.3, no., pp.1367-1370 , 7-10 .
Huo Guo-ping; Miao Ling-juan; Ding Hui, "A new method for efficient design of Butterworth filter based on symbolic calculus," in Proc. International Conference on Computer Application and System Modeling (ICCASM), 2010, vol.8, no., pp., 22-24 .
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