Data from: Animal Sound Identifier (ASI): software for automated identification of vocal animals

Automated audio recording offers a powerful tool for acoustic monitoring
schemes of bird, bat, frog and other vocal organisms, but the lack of
automated species identification methods has made it difficult to fully
utilise such data. We developed Animal Sound Identifier (ASI), a MATLAB
software that performs probabilistic classification of species occurrences
from field recordings. Unlike most previous approaches, ASI locates
training data directly from the field recordings and thus avoids the need
of pre‐defined reference libraries. We apply ASI to a case study on
Amazonian birds, in which we classify the vocalisations of 14 species in
194 504 one‐minute audio segments using in total two weeks of expert time
to construct, parameterise, and validate the classification models. We
compare the classification performance of ASI (with training templates
extracted automatically from field data) to that of monitoR (with training
templates extracted manually from the Xeno‐Canto database), the results
showing ASI to have substantially higher recall and precision rates.