PNW-Cnet v4: Automated species identification for passive acoustic monitoring

Abstract

We present PNW-Cnet v4, a deep neural net with an associated Shiny-based application designed to facilitate efficient data processing to detect terrestrial wildlife species through passive acoustic monitoring. PNW-Cnet v4 is a deep convolutional neural network that detects audio signatures of 37 focal species of birds and mammals that inhabit forests of the Pacific Northwest, USA, along with other commonly occurring forest sounds. The primary objective of developing PNW-Cnet v4 was to support a long-term northern spotted owl (Strix occidentalis caurina) monitoring program. By incorporating additional species classes, PNW-Cnet v4 expands applicability of the program to broadscale biodiversity research and monitoring. Using the Shiny app with PNW-Cnet v4, users can process audio data using a graphical user interface, summarize apparent detections visually, and export results in tabular format.

Keywords

• Bioacoustics
• Machine learning
• Automated detection
• Wildlife monitoring
• Ecology
• Biodiversity