Automated detection and classification of birdsong: An ensemble approach

Brooker, Stuart A.; Stephens, Philip A.; Whittingham, Mark J.; Willis, Stephen G.

Abstract

The avian dawn chorus presents a challenging opportunity to test autonomous recording units (ARUs) and associated recogniser software in the types of complex acoustic environments frequently encountered in the natural world. To date, extracting information from acoustic surveys using readily-available signal recognition tools (‘recognisers’) for use in biodiversity surveys has met with limited success. Combining signal detection methods used by different recognisers could improve performance, but this approach remains untested. Here, we evaluate the ability of four commonly used and commercially- or freely-available individual recognisers to detect species, focusing on five woodland birds with widely-differing song-types. We combined the likelihood scores (of a vocalisation originating from a target species) assigned to detections made by the four recognisers to devise an ensemble approach to detecting and classifying birdsong. We then assessed the relative performance of individual recognisers and that of the ensemble models. The ensemble models out-performed the individual recognisers across all five song-types, whilst also minimising false positive error rates for all species tested. Moreover, during acoustically complex dawn choruses, with many species singing in parallel, our ensemble approach resulted in detection of 74% of singing events, on average, across the five song-types, compared to 59% when averaged across the recognisers in isolation; a marked improvement. We suggest that this ensemble approach, used with suitably trained individual recognisers, has the potential to finally open up the use of ARUs as a means of automatically detecting the occurrence of target species and identifying patterns in singing activity over time in challenging acoustic environments.

Citation

Brooker, S. A., Stephens, P. A., Whittingham, M. J., & Willis, S. G. (2020). Automated detection and classification of birdsong: An ensemble approach. Ecological Indicators, 117, Article 106609. https://doi.org/10.1016/j.ecolind.2020.106609

• Journal Article Type: Article
• Acceptance Date: Jun 3, 2020
• Online Publication Date: Jun 17, 2020
• Publication Date: 2020-10
• Deposit Date: Jun 18, 2020
• Publicly Available Date: Jun 18, 2020
• Journal: Ecological Indicators
• Print ISSN: 1470-160X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 117
• Article Number: 106609
• DOI: https://doi.org/10.1016/j.ecolind.2020.106609
• Public URL: https://durham-repository.worktribe.com/output/1262452