Radial basis function neural networks and temporal fusion for the classification of bioacoustic time series

Neurocomputing

Volumn 51, Issue , 2003, Pages 265-275

  Schwenker, Friedhelm ^a   Dietrich, Christian ^a   Kestler, Hans A ^a   Riede, Klaus ^b   Palm, Günther ^a  

^a UNIVERSITY OF ULM   (Germany)

^b Zoological Inst of Sci Res/Museum   (Germany)

Author keywords

Bioacoustics; Classifier fusion; Radial basis function networks; Temporal fusion

Indexed keywords

DATA ACQUISITION; NEUROLOGY;

BIOACOUSTIC TIME SERIES;

RADIAL BASIS FUNCTION NETWORKS;

ACOUSTICS; ARTICLE; ARTIFICIAL NEURAL NETWORK; CALCULATION; CLASSIFICATION; CONTROLLED STUDY; DATA BASE; DECISION MAKING; ECUADOR; INFORMATION PROCESSING; INSECT; NONHUMAN; PRIORITY JOURNAL; RECORDING; SOUND DETECTION; SPECIES; THAILAND; TIME;

EID: 0037382120     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0925-2312(02)00621-5     Document Type: Article

References (26)

1. C.M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, New York, 1995.
2. C. Dietrich, F. Schwenker, G. Palm, Classification of time series utilizing temporal and decision fusion, in: J. Kittler, F. Roli (Eds.), Multiple Classifier Systems, Springer, Berlin, 2001a, pp. 378-387.
3. C. Dietrich, F. Schwenker, K. Riede, G. Palm, Classification of bioacoustic time series utilizing pulse detection, time and frequency features and data fusion, www.informatik.uni-ulm.de/pw/berichte Ulmer Informatik-Berichte 2001-04, University of Ulm, 2001b.
4. M.R. Evans, J.A. Evans, A computer-based technique for the quantitative analysis of animal sounds, Internat. J. Animal Sound Recording 5 (1994) 281-290.
5. M.I. Glavinovic, Comparison of parzen density and frequency histogram as estimators of probability density functions, European J. Physiology 433 (1) (1996) 174-179.
6. G. Grigg, A. Taylor, H. Mc Callum, Graeme Watson, Monitoring frog communities: an application of machine learning, Proceedings of the Eighth Innovative Applications of Artificial Intelligence Conference, Portland, OR, 1996, pp. 1564-1569.
7. R.J. Howlett, L.C. Jain, Radial Basis Function Networks 1: Recent Developments in Theory and Applications. Studies in Fuzziness and Soft Computing, Springer, Berlin, 2001a.
8. R.J. Howlett, L.C. Jain, Radial Basis Function Networks 2: New Advances in Design. Studies in Fuzziness and Soft Computing, Springer, Berlin, 2001.
9. S. Ingrisch, Taxonomy, stridulation and development of Podoscirtinae from Thailand, Senckenbergiana Biol. 77 (1997) 47-75.
10. J. Kittler, M. Hatef, R.P.W. Duin, J. Matas, On combining classifiers, IEEE Trans. Pattern Anal. Mach. Intell. 20 (3) (1998) 226-239.
11. J.A. Kogan, D. Margoliash, Automated recognition of bird song elements from continuous recordings using dynamic time warping and hidden Markov models: a comparative study J. Acoust. Soc. Amer. 103 (4) (1998) 2185-2196.
12. T. Kohonen, Self-Organizing Maps, Springer, Berlin, 1995.
13. L.I. Kuncheva, On combining multiple classifiers, Proceedings of the seventh International Conference on Information Processing and Management Uncertainty, Paris, 1998, pp. 1890-1891.
14. S.O. Murray, E. Mercado, H.L. Roitblat, The neural network classification of false killer whale (Pseudorca crassidens) vocalizations, J. Acoust. Soc. Amer. 104 (6) (1998) 3626-3633.
15. F. Nischk, Die Grillengesellschaften zweier neotropischer Waldökosysteme in Ecuador, Ph.D. Thesis, University of Köln, Germany, 1999, Memorandum UCB/ERL-M89/29 (in German).
16. T. Poggio, F. Girosi, Networks for approximation and learning, Proc. IEEE 78 (9) (1990) 1481-1497.
17. J.R. Quinlan, C4.5 Programs for Machine Learning, Morgan Kaufmann, San Mateo, 1993.
18. N. Rabiner, L.Y. Juang, Fundamentals of Speech Recognition, PTR Prentice-Hall, NJ, 1993.
19. L.R. Rabiner, S.E. Sambur, An algorithm for determining the endpoints of isolated utterances, Bell Syst. Tech. J. 54 (2) (1975) 297-315.
20. K. Riede, Acoustic monitoring of orthoptera and its potential for conversation, J. Insect Conservation 2 (1998) 217-223.
21. B.D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge, 1996.
22. V.D. Sanchez (Ed.), Neurocomputing Special Issue on RBF Networks, Part I, Vol. 19 (1-3), 1998a.
23. V.D. Sanchez (Ed.), Neurocomputing Special Issue on RBF Networks, Part II, Vol. 20 (1-3), 1998b.
24. V.D. Sanchez, Neural network based pattern recognition, in: S.K. Pal, et al. (Eds.), Pattern Recognition - From Classical to Modern Approaches, World Scientific, Singapore, 2001, pp. 281-300.
25. F. Schwenker, H.A. Kestler, G. Palm, Three phase learning for radial basis function networks, Neural Networks 14 (2001) 439-458.
26. D.M.J. Tax, M. van Bruecklen, R.P.W. Duin, J. Kittler, Combining multiple classifiers by averaging or by multiplying? Pattern Recognition 33 (2000) 1475-1485.