Evaluation of Algorithms for Sequential Decision in Biosonar Target Classification
A sequential decision problem, based on the task ofidentifying the species of trees given acoustic echo data collectedfrom them, is considered with well-known stochastic classifiers,including single and mixture Gaussian models. Echoes are processedwith a preprocessing stage based on a model of mammalian cochlearfiltering, using a new discrete low-pass filter characteristic. Stoppingtime performance of the sequential decision process is evaluated andcompared. It is observed that the new low pass filter processingresults in faster sequential decisions.
[1] P. McKerrow, and N. Harper, ''Plant acoustic density profile model of CTFM ultrasonic sensing,'' IEEE Sensors Journal, vol. 1, no. 4, pp.245-255, Dec. 2001.[2] R. M├╝ller, and R. Kuc, ''A parsimonious signal representation of random echoes based on a biomimetic spike code,'' in Proc. ICSC Symp. On Intelligent Systems and Applications, pp. 915-921,Canada,May 2000.[3] C. W. Baum and V. V. Veeravalli, ''A sequential procedure for multihypothesis testing,'' IEEE Trans. Information Theory, vol. 40, no.6, Nov. 1994.[4] A. G. Tartakovsky, X. R. Li, and G. Yaralov, ''Sequential detection of targets in multichannel systems,'' IEEE Trans. Information Theory, vol.49, no. 2, Feb. 2003.[5] R. M├╝ller, ''A computational theory for the classification natural biosonar targets based on a spike code,'' Network: Comput. Neural Syst., vol. 14, pp. 595-612, May 2003.[6] C. Therrien, Decision, Estimation and Classification, An Introductionto Pattern Recognition and Other Related Topics, John wiley & Sons,1989.[7] H. Akaike, ''A new look at the statistical model identification,'' IEEE Trans. Automatic Control, vol. 19, no. 6, pp. 716-723, Dec. 1974.[8] J. Rissanen, ''Stochastic complexity and modeling,'' The Annals of Statistics, vol. 13, no. 3, pp. 1080-1100, 1986.[9] A. P. Demspter, N. A. Laird, and D. B. Rubin, ''Maximum likelihood from incomplete data via the EM algorithm,'' Journal of the Royal Statistical Society Series B, vol. 39, pp. 1-38, 1977.[10] T. Temel, and J. Hallam, ''An evaluation of algorithms for single-echo biosonar target classification,'' submitted to ICCI'04.[11] C. M. Bishop, Neural Networks for Pattern Recognition, Oxford Univ.Press, 1995.[12] Y. Linde, A. Buzo, and R. M. Gray, ''An algorithm for vector quantizer design,'' IEEE Trans. Commun., vol. COM-28, pp. 84-95, Dec. 1980.
[1] P. McKerrow, and N. Harper, ''Plant acoustic density profile model of CTFM ultrasonic sensing,'' IEEE Sensors Journal, vol. 1, no. 4, pp.245-255, Dec. 2001.[2] R. M├╝ller, and R. Kuc, ''A parsimonious signal representation of random echoes based on a biomimetic spike code,'' in Proc. ICSC Symp. On Intelligent Systems and Applications, pp. 915-921,Canada,May 2000.[3] C. W. Baum and V. V. Veeravalli, ''A sequential procedure for multihypothesis testing,'' IEEE Trans. Information Theory, vol. 40, no.6, Nov. 1994.[4] A. G. Tartakovsky, X. R. Li, and G. Yaralov, ''Sequential detection of targets in multichannel systems,'' IEEE Trans. Information Theory, vol.49, no. 2, Feb. 2003.[5] R. M├╝ller, ''A computational theory for the classification natural biosonar targets based on a spike code,'' Network: Comput. Neural Syst., vol. 14, pp. 595-612, May 2003.[6] C. Therrien, Decision, Estimation and Classification, An Introductionto Pattern Recognition and Other Related Topics, John wiley & Sons,1989.[7] H. Akaike, ''A new look at the statistical model identification,'' IEEE Trans. Automatic Control, vol. 19, no. 6, pp. 716-723, Dec. 1974.[8] J. Rissanen, ''Stochastic complexity and modeling,'' The Annals of Statistics, vol. 13, no. 3, pp. 1080-1100, 1986.[9] A. P. Demspter, N. A. Laird, and D. B. Rubin, ''Maximum likelihood from incomplete data via the EM algorithm,'' Journal of the Royal Statistical Society Series B, vol. 39, pp. 1-38, 1977.[10] T. Temel, and J. Hallam, ''An evaluation of algorithms for single-echo biosonar target classification,'' submitted to ICCI'04.[11] C. M. Bishop, Neural Networks for Pattern Recognition, Oxford Univ.Press, 1995.[12] Y. Linde, A. Buzo, and R. M. Gray, ''An algorithm for vector quantizer design,'' IEEE Trans. Commun., vol. COM-28, pp. 84-95, Dec. 1980.
@article{"International Journal of Information, Control and Computer Sciences:57411", author = "Turgay Temel and John Hallam", title = "Evaluation of Algorithms for Sequential Decision in Biosonar Target Classification", abstract = "A sequential decision problem, based on the task ofidentifying the species of trees given acoustic echo data collectedfrom them, is considered with well-known stochastic classifiers,including single and mixture Gaussian models. Echoes are processedwith a preprocessing stage based on a model of mammalian cochlearfiltering, using a new discrete low-pass filter characteristic. Stoppingtime performance of the sequential decision process is evaluated andcompared. It is observed that the new low pass filter processingresults in faster sequential decisions.
", keywords = "Classification, neuro-spike coding, parametricmodel, Gaussian mixture with EM algorithm, sequential decision.", volume = "1", number = "1", pages = "128-4", }
