Automated recognition of ruffed grouse drumming in field recordings

• Published in: Wildlife Society bulletin (2011) Vol. 47; no. 1
• Main Authors: Lapp, Samuel, Larkin, Jeffery L., Parker, Halie A., Larkin, Jeffery T., Shaffer, Dakotah R., Tett, Carolyn, McNeil, Darin J., Fiss, Cameron J., Kitzes, Justin
• Format: Journal Article
• Language: English
• Published: Wiley 01.03.2023
• Subjects: acoustic monitoring; ARU; automated recognition; Bonasa umbellus; drumming; machine learning; ruffed grouse; signal processing
• ISSN: 2328-5540; 2328-5540
• DOI: 10.1002/wsb.1395

Ruffed grouse (Bonasa umbellus) populations are declining throughout their range, which has prompted efforts to understand drivers of the decline. Ruffed grouse monitoring efforts often rely on acoustic drumming surveys, in which a surveyor listens for the distinctive drumming sound that male ruffed grouse produce during the breeding season. Field‐based drumming surveys can fail to detect ruffed grouse when the birds drum infrequently or irregularly, making this species an excellent candidate for remote acoustic sensing with automated recording units (ARUs). An accurate automated recognition method for ruffed grouse drumming could enable effective and efficient use of ARU data for monitoring efforts; however, no such tool is currently available. Here we develop an automated method for detecting ruffed grouse drumming in audio recordings. Our detector uses a signal processing pipeline designed to recognize the accelerating pattern of drumming. We show that the automated recognition method accurately and efficiently detects drumming events in a set of labeled ARU field recordings. In a case study with 56 locations in Central Pennsylvania, we compared detections of ruffed grouse from 4 survey methods: field‐based acoustic drumming surveys, surveys conducted by humans listening to ARU recordings, and automated recognition for both a 1‐day and a 28‐day period. Field‐based surveys detected drumming at 9 of 56 locations (16%), while surveys conducted by humans listening to ARU recordings detected drumming at 8 locations (14%). Using automated recognition, the 1‐day recording period produced detections at 17 locations (30%) and the 28‐day recording period produced detections at 34 locations (61%). Our case study supports the idea that automated recognition can unlock the value of ARU datasets by temporally expanding the survey period. We provide an open‐source Python implementation of the recognition method to support further use in ruffed grouse monitoring efforts. A novel automated recognition method uses signal processing to identify ruffed grouse drumming in audio recordings. In our case study, using the automated method on field recordings produces more detections than field‐based human point counts and requires minimal manual review.