Classifying Animal Sounds with Neural Networks

Humans naturally classify the sounds they hear into different categories, including sounds produced by animals. Bioacousticians have supplemented this type of subjective sorting with quantitative analyses of acoustic features of animal sounds. Using neural networks to classify animal sounds extends this process one step further by not only facilitating objective descriptive analyses of animal sounds, but also by making it possible to simulate auditory classification processes. Critical aspects of developing a neural network include choosing a particular architecture, converting measurements into input representations, and training the network to recognize inputs. When the goal is to sort vocalizations into specific types, supervised learning algorithms make it possible for a neural network to do so with high accuracy and speed. When the goal is to sort vocalizations based on similarities between measured properties, unsupervised learning algorithms can be used to create neural networks that objectively sort sounds or that quantify sequential properties of sequences of sounds. Neural networks can also provide insights into how animals might themselves classify the sounds they hear, and be useful in developing specific testable hypotheses about the functions of different sounds. The current chapter illustrates each of these applications of neural networks in studies of the sounds produced by chickadees (Poecile atricapillus), false killer whales (Pseudoorca crassidens), and humpback whales (Megaptera novaeangliae).

Keywords: Adaptive filter, Computational modeling, Connectionism, Learning algorithm, Parallel distributed processing, Perceptron, Self-organizing.