Environmental Acoustics Modelling Techniques for Forest Monitoring

Abstract

Environmental sounds detection plays an increasing role in computer science and robotics as it simulates the human faculty of hearing. It is applied in environment research, monitoring and protection, by allowing investigation of natural reserves, and showing potential risks of damage that can be deduced from the environmental acoustic. The research presented in this paper is related to the development of an intelligent forest environment monitoring solution, which applies signal analysis algorithm to detect endangering sounds. Environmental sounds are processed using some modelling algorithms based on which the acoustic forest events can be classified into one of the categories: chainsaw, vehicle, genuine forest background noise. The article will explore and compare several methodologies for environmental sound classification, among which the dominant Deep Neural Networks, the Long Short-Term Memory, and the classical Gaussian Mixtures Modelling and Dynamic Time Warping.

A Survey on Artificial Intelligence-Based Acoustic Source Identification

“…Due to the system's interpretability, it can be evaluated and debugged easily. Moreover, the authors in [197] proposed an intelligent forest monitoring system that applies signal processing techniques such as dynamic time warping and ML algorithms trained by MFCCs and spectral feature spaces.…”