On-bird Sound Recordings: Automatic Acoustic Recognition of Activities and Contexts

TL;DR

This paper presents methods for automatically recognizing activities and contexts from on-bird audio recordings using sound scene analysis, enabling scalable annotation of animal behavior data despite individual variability.

Contribution

It introduces two sound scene analysis paradigms for annotating bird backpack recordings, advancing automatic behavior recognition in field animal studies.

Findings

Recognition quality comparable to state-of-the-art methods

Partial annotation enables scalable automatic data analysis

Individual differences limit generalization across animals

Abstract

We introduce a novel approach to studying animal behaviour and the context in which it occurs, through the use of microphone backpacks carried on the backs of individual free-flying birds. These sensors are increasingly used by animal behaviour researchers to study individual vocalisations of freely behaving animals, even in the field. However such devices may record more than an animals vocal behaviour, and have the potential to be used for investigating specific activities (movement) and context (background) within which vocalisations occur. To facilitate this approach, we investigate the automatic annotation of such recordings through two different sound scene analysis paradigms: a scene-classification method using feature learning, and an event-detection method using probabilistic latent component analysis (PLCA). We analyse recordings made with Eurasian jackdaws (Corvus monedula) in both captive and field settings. Results are comparable with the state of the art in sound scene analysis; we find that the current recognition quality level enables scalable automatic annotation of audio logger data, given partial annotation, but also find that individual differences between animals and/or their backpacks limit the generalisation from one individual to another. we consider the interrelation of 'scenes' and 'events' in this particular task, and issues of temporal resolution.