ABSTRACT:
This work presents a scheme for the detection of manatee vocalizations in underwater recordings to support efforts in monitoring and population estimation of this species in western Panama. The proposed automatic detection scheme uses the autoregressive model as a feature extraction stage to feed two-layer feedforward neural networks that classify the signal as vocalizations or background noise. The neural network was trained with the scaled conjugate gradient backpropagation algorithm using supervised learning. The proposed scheme provides an accuracy of 92.4% on the training set for both classes.

AUTHORS
Guillaume Ferré
Hector M. Guzman