Using intrinsic and contextual information associated with automated signal detections to improve call recognizer performance: A case study using the cryptic and critically endangered Night Parrot Pezoporus occidentalis

摘要

1 Rapid expansion in the collection of large acoustic datasets to answer ecological questions has generated a parallel requirement for techniques that streamline analysis of these datasets. In many cases, automated signal recognition algorithms, often termed 'call recognizers', are the only feasible option for doing this. To date, most research has focused on what types of recognizers perform best, and how to train these recognizers to optimize performance.