Deep Learning Acoustic Monitoring of Bird Diversity in Fragmented Forests

Abstract

Biodiversity of birds is one of the most significant indices of ecological condition, whereas fragmentation may have significant impacts on the habitat structure, species' composition and ecological relationships. Traditional techniques for bird biodiversity monitoring such as direct observations and manual acoustic recognition are usually quite costly and time-consuming processes. This paper suggests the development of the deep learning technique of bird diversity assessment based on acoustic monitoring in fragmented forests. The suggested technique employs passive acoustic recordings obtained from various fragments of forests and involves acoustic recordings processing, noise filtering, segmentation and spectrograms extraction of the acoustic signals. Machine learning methods are then applied to detect and classify bird sounds and determine the species in a complicated audio environment. The detected species will be used to measure and analyze bird biodiversity in different forest patches. This strategy aims to enhance the effectiveness and scalability of biodiversity monitoring and minimize human involvement in this process. This research analyzes the influence that differences in acoustic behavior and species presence may have on understanding the ecological impacts of forest fragmentation. The suggested framework will be helpful for wildlife scientists, conservationists, and forest managers in developing automatic approaches for assessing bird biodiversity