Multiple Classifier Systems for the Recognition of Orthoptera Songs

The classification of bioacoustic time series is topic of this paper. In particular, we discuss the combination of local classifier decisions from several feature spaces with static and adaptable fusion schemes, e.g. averaging, voting and decision templates. We present static fusion schemes and algorithms to calculate decision templates, and demonstrate the behaviour of both approaches to bioacoustic applications, the classification of insect songs. Results of these algorithms are presented for species of crickets and katydids. Both families are members of the insect order Orthoptera.

[1]  Josef Kittler,et al.  Combining multiple classifiers by averaging or by multiplying? , 2000, Pattern Recognit..

[2]  Andrew Taylor,et al.  Monitoring Frog Communities: An Application of Machine Learning , 1996, AAAI/IAAI, Vol. 2.

[3]  H L Roitblat,et al.  The neural network classification of false killer whale (Pseudorca crassidens) vocalizations. , 1998, The Journal of the Acoustical Society of America.

[4]  J A Kogan,et al.  Automated recognition of bird song elements from continuous recordings using dynamic time warping and hidden Markov models: a comparative study. , 1998, The Journal of the Acoustical Society of America.

[5]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[6]  Günther Palm,et al.  Decision templates for the classification of bioacoustic time series , 2002, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing.

[7]  Günther Palm,et al.  Decision templates for the classification of bioacoustic time series , 2003, Inf. Fusion.

[8]  Ludmila I. Kuncheva,et al.  Using measures of similarity and inclusion for multiple classifier fusion by decision templates , 2001, Fuzzy Sets Syst..

[9]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[10]  Günther Palm,et al.  Classification of Time Series Utilizing Temporal and Decision Fusion , 2001, Multiple Classifier Systems.

[11]  James C. Bezdek,et al.  Decision templates for multiple classifier fusion: an experimental comparison , 2001, Pattern Recognit..