Live Acoustic Monitoring of Forests to Detect Illegal Logging and Animal Activity

Abstract

Illegal cutting of trees and poaching in the forest has become a serious issue regarding environmental conservation. Trespassing in the forest has an adverse effect on the habitat of animals. There is no effective solution for real-time detection and warning of such activity. Image-based monitoring solutions are too costly and cannot cover a wide range of areas. A novel approach of audio-based monitoring systems using deep neural learning can be proposed as a solution to this problem. A model has to be trained using various audio samples of cutting of trees, gunshots, etc., along with the outliers. There are numerous tree felling techniques and hunting techniques. In the case of methods that are known to the model, the model detects that event and hence warns the authorities. The audio samples in the dataset in the time domain are converted to the frequency domain using fast Fourier transform (FFT). This distributes the signal across corresponding frequencies. For better visualization of features, it is then converted into a Mel scale, and the spectrum of this spectrum is computed using cosine transformation to obtain the Mel-frequency cepstral coefficients. Relevant features are then extracted using these coefficients and classify them using the proposed deep neural learning method. There is a significant difference between the energy concentration distributions of the sound that has to be detected with that of the outliers. This enables to classify the audio samples with a greater signal-to-noise ratio. The resulting model is then used for live monitoring of forests against illegal activities. The current situation of the wildlife demands an accurate database of animal activity in a particular area. This helps both the wildlife tourism and various studies. For addressing this issue, the proposed model is also trained to detect the presence of animals, and it will accomplish it without disturbing the wildlife activity.