Year: 2023
Type: article
Abstract: Landscape‐scale bioacoustic projects have become a popular approach to biodiversity monitoring. Combining passive acoustic monitoring recordings and automated detection provides an effective means of monitoring sound‐producing species' occupancy and phenology and can lend insight into unobserved behaviours and patterns. The availability of low‐cost recording hardware has lowered barriers to large‐scale data collection, but technological barriers in data analysis remain a bottleneck for extracting biological insight from bioacoustic datasets. We provide a robust and open‐source Python toolkit for detecting and localizing biological sounds in acoustic data. OpenSoundscape provides access to automated acoustic detection, classification and localization methods through a simple and easy‐to‐use set of tools. Extensive documentation and tutorials provide step‐by‐step instructions and examples of end‐to‐end analysis of bioacoustic data. Here, we describe the functionality of this package and provide concise examples of bioacoustic analyses with OpenSoundscape. By providing an interface for bioacoustic data and methods, we hope this package will lead to increased adoption of bioacoustics methods and ultimately to enhanced insights for ecology and conservation.
Source: Methods in Ecology and Evolution
Institution: University of Pittsburgh
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: gold
Estimated APC paid: $3,500
Awards: IIBR Multidisciplinary: Locating and counting terrestrial wildlife with an open source, automated acoustic survey platform, G162766112, BII: Uncovering mechanisms of amphibian resilience to global change from molecules to landscapes, G3226045097, G3612148187, G4841637794, G5603904134, G7206802296
OpenAlex ID: W4385709007