title: Quantifying bat call detection performance of humans and machines.
abstract: Methods for detecting echolocation calls in field recordings of bats vary in performance and influence the effective range of a recording system. In experiments using synthetic calls from five species, human detection accuracy was 89.7+/-0.6%, compared to 76.3+/-0.8% for a model-based detector, 72.2+/-0.8% for an energy-based detector, and 98.4+/-0.2% for an optimal linear detector. The energy-based detector was 11 times faster than the model-based detector and 110 times faster than humans. Human accuracy was positively correlated with test duration (R(2)=0.43, P<0.05), meaning that higher accuracy was achieved at the expense of slower performance. Species was a significant factor determining accuracy for all detectors (P<0.001) because of call bandwidth: Narrowband calls concentrated energy in a narrower frequency band and were easier to detect. For a hypothetical recording system, range at 90% human detection accuracy varied from 10 to 35 m among species, while range dropped by approximately 20% using the automated detectors. The optimal detector outperformed humans by 5 dB and the automated methods by 9 dB. The results quantify the tradeoff between detector speed and accuracy and are useful for designing field studies of bats.
authors.affiliation: Department of Biology, University of Western Ontario, London, Ontario, Canada. mskowro2@uwo.ca
authors.forename: Mark D
authors.initials: MD
authors.lastname: Skowronski
authors.forename: M Brock
authors.initials: MB
authors.lastname: Fenton
mesh_terms: D000818:Animals; D001307:Auditory Perception; D002685:Chiroptera; D004455:Echolocation; D001330:Electronic Data Processing; D006801:Humans; D008962:Models, Theoretical; D017603:Signal Detection, Psychological; D014828:Vocalization, Animal