Passive Acoustic Monitoring

Passive acoustic monitoring (PAM) uses sound recordings to study animal populations and environmental conditions, offering a cost-effective and minimally invasive approach to biodiversity assessment. Current research emphasizes improving automated analysis of PAM data, focusing on machine learning techniques like convolutional recurrent neural networks (CRNNs) and deep learning models, often incorporating active learning strategies to address data scarcity and improve model generalizability across diverse environments. These advancements are crucial for efficient processing of large datasets and enabling broader applications in conservation, ecological monitoring, and even precision agriculture, ultimately enhancing our understanding of ecosystems and informing management strategies.