Poster Abstract: A Framework for Chainsaw Detection Using One-Class and WSNs

The Amazon Rainforest degradation is a worldwide concern. The rainforest has been endangered by the illegal wood extraction without control even in the preservation areas. Due to the large geography extension prevent these crimes with an unmanned aerial vehicle (UAV) is not always possible. The Wireless Acoustics Sensor Network (WASNs) technology can alleviate this problem. Here, we present an acoustical framework to detect the sounds produced by several chainsaws. Our framework was developed to be embedded in a sensor node, combining the Mel-Fourier Cepstral Coefficients (MFCCs) with One-class classification technique. This classification method, that is based on a kernel density approach, 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 WSN application.