Abstract
We evaluated the potential of using convolutional neural networks in classifying spectrograms of Antillean manatee (Trichechus manatus manatus) vocalizations. Spectrograms using binary, linear and logarithmic amplitude formats were considered. Two deep convolutional neural networks (DCNN) architectures were tested: linear (fixed filter size) and pyramidal (incremental filter size). Six experiments were devised for testing the accuracy obtained for each spectrogram representation and architecture combination. Results show that binary spectrograms with both linear and pyramidal architectures with dropout provide a classification rate of 94–99% on the training and 92–98% on the testing set, respectively. The pyramidal network presents a shorter training and inference time. Results from the convolutional neural networks (CNN) are substantially better when compared with a signal processing fast Fourier transform (FFT)-based harmonic search approach in terms of accuracy and F1 Score. Taken together, these results prove the validity of using spectrograms and using DCNNs for manatee vocalization classification. These results can be used to improve future software and hardware implementations for the estimation of the manatee population in Panama.
Highlights
In western Caribbean Panama, rivers and wetlands with abundant aquatic vegetation attract marine herbivores such as the Antillean manatee (Trichechus manatus manatus).This species is listed as endangered by the International Union for the Conservation of Nature (IUCN), showing a decreasing regional population trend updated over a decade ago [1]
In terms of accuracy all models were able to achieve over 94% results after 50 epochs for the testing set, with a few 100% for the linear spectrogram representation
A preliminary conclusion of Experiments #1 and #2 was that convolutional neural networks (CNN) can be applied for the analysis of vocalization in classification tasks, with an accuracy of close to 94%, even in the validation set
Summary
In western Caribbean Panama, rivers and wetlands with abundant aquatic vegetation attract marine herbivores such as the Antillean (or Caribbean) manatee (Trichechus manatus manatus). This species is listed as endangered by the International Union for the Conservation of Nature (IUCN), showing a decreasing regional population trend updated over a decade ago [1]. Threats includes low genetic variability [2] and external factors such as illegal hunting, habitat pollution and degradation and watercraft collisions [1,3]. Population assessment and understanding how manatees use their habitat are fundamental requirements to restore and manage the populations of Antillean manatees at local and regional levels.
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