Automated recognition of ruffed grouse drumming in field recordings

Abstract

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.

OpenSoundscape: An open‐source bioacoustics analysis package for Python

“…• signal_processing module: Access a set of signal processing tools, including the find_accel_sequences function used to detect Ruffed Grouse drumming in (Lapp, Larkin, et al, 2023).…”

“…Top‐level API components: CNN class: Train CNNs with custom parameters and flexible architecture; generate predictions on audio data; save and load trained models; monitor training and inference progress and metrics through integration with the Weights and Biases platform (Biewald, 2020). signal_processing module: Access a set of signal processing tools, including the find_accel_sequences function used to detect Ruffed Grouse drumming in (Lapp, Larkin, et al, 2023). localization module: Spatially localize audio events from time‐synchronized recordings using the SynchronizedRecorderArray or SpatialEvent classes. ribbit function: Detect sounds with periodic amplitude modulation using the method described in (Lapp et al, 2021).…”

“…Extensive package documentation and tutorials for OpenSoundscape provide step‐by‐step instructions and examples for manipulating audio and automating the recognition of biological sounds. OpenSoundscape has already been used in a variety of bioacoustics applications, from exploratory data analysis to automated recognition of frogs (Lapp et al, 2021), birds (Lapp, Larkin, et al, 2023; Malamut, 2022) and gunshots (Katsis et al, 2022) through various signal processing and deep learning methods.…”