MARINE MAMMAL CALL DISCRIMINATION USING ARTIFICIAL NEURAL NETWORKS

Recent work has applied a linear spectrogram correlator filter (SCF) to detect bowhead whale (Balaena mysticetus) song notes, outperforming both a time-series-matched filter and a hidden Markov model. The method relies on an empirical weighting matrix. An artificial neural net (ANN) may be better yet, since it offers two advantages; (i) the equivalent weighting matrix is determined by training and can converge to a more optimal solution and (ii) an ANN isa nonlinear estimator and can embody more sophisticated responses. A three-layer feed-forward ANN is ideally suited to this application and has been implemented on 1475 sounds, of which 54% were used for training and 46% kept as ''unseen'' test data. The trained ANN error rate was 1.5%, a twofold improvement over previous methods. It is shown that ANN hidden neurons can be interrogated to reveal the operating paradigm developed during training. The function of each of these neurons can be determined in terms of spectrographic features of the training calls. Furthermore, the operating paradigm can be controlled and training time reduced by assigning specific recognition tasks to hidden neurons prior to training, rather than initiating training with randomized weights. The ANN is compared to the SCF and the role of the ''hidden'' neurons and equivalent weighting matrices are discussed.