A Framework for Chainsaw Detection Using One-Class Kernel and Wireless Acoustic Sensor Networks into the Amazon Rainforest

In this work we present a framework for automatic acoustic detection of chainsaw sounds to detect illegal wood extraction in the Amazon Rainforest. Our approach was developed to be embedded into the sensor nodes of a Wireless Acoustic Sensor Network (WASN) to monitor the environment. First, we represent each sound by a set of Mel-Frequency Cepstral Coefficients (MFCCs) and, after that, we fit a Probability Density Function (PDF) with a kernel based on a multivariate Gaussian density estimation using only the target class. This One-Class classification method allows us to recognize only chainsaw sounds rejecting all the other possible environmental sounds, such as: animal's calls, weather noises or boat engines. In the experiments, we varied the number MFCCs coefficients and the Kernel bandwidth performing a leave-one-out cross validation to find the best combination. Finally, we found that the best parameter combination achieve 98% of accuracy showing a low FNR and a high TPR, fact that enhances the credibility of the system avoiding false alarms and making it an optimal choice for an WASN application.