An Open-Access System for Long-Range Chainsaw Sound Detection

Abstract

A pipeline for automatic detection of chainsaw events in audio recordings is presented as the means to detect illegal logging activity in a protected natural environment. We propose a two-step process that consists of an activity detector at the front end and a deep neural network (DNN) classifier at the back end. At the front end, we use the Summation or Residual Harmonics method in order to detect patterns with harmonic structure in the audio recording. Active audio segments are consequently fed to the classifier that decides upon the absence or presence of a chainsaw event. As acoustic feature, we propose the widely-used amplitude spectrogram, passing it through the recently proposed Per-Channel Energy Normalization (PCEN) process. Results based on real-field recordings illustrate that the proposed end-to-end system may efficiently detect low-SNR chainsaw events at a very low false detection rate.