A Distributed Approach for Classifying Anuran Species Based on Their Calls

In this work, we evaluate the performance of a distributed classification system in a Wireless Sensor Network for monitoring anurans. Our aim is to study how to take advantage of the collaborative nature of the sensor network to improve the recognition of anuran calls. To accomplish this, we evaluate four low-cost techniques (majority vote, weighted majority vote, arithmetic and geometric combinators) to combine three classifiers commonly used in sensor applications (Quadratic Discriminant Analysis, Naive Bayes, and Decision Trees) and trained to identify anuran calls. We investigate how the environment perceptions of the sensors can be used to discard confusing scenarios, i.e., scenarios in which there are multiple calls from different species at same time. Our best combination strategy achieved a gain of about 11% over a sensor taken in isolation. We also found that, by using the entropy of the species estimates, the sensor committee is able to effectively identify confusing scenarios, increasing gains over the isolated sensor to about 20%.