López-Baucells, A, Torrent, L, Rocha, R, Bobrowiec, PED, Palmeirim, JM and Meyer, CFJ 
ORCID: https://orcid.org/0000-0001-9958-8913
2018,
'Stronger together : combining automated classifiers with manual post-validation optimizes the workload vs reliability trade-off of species identification in bat acoustic surveys'
, Ecological Informatics, 49 (Jan 19)
, pp. 45-53.
Abstract
Owing to major technological advances, bioacoustics has become a burgeoning field in
ecological research worldwide. Autonomous passive acoustic recorders are becoming widely
used to monitor aerial insectivorous bats, and automatic classifiers have emerged to aid
researchers in the daunting task of analyzing the resulting massive acoustic datasets.
However, the scarcity of comprehensive reference call libraries still hampers their wider
application in highly diverse tropical assemblages. Capitalizing on a unique acoustic dataset
of more than 650,000 bat call sequences collected over a 3-year period in the Brazilian
Amazon, the aims of this study were (a) to assess how pre-identified recordings of free-flying
and hand-released bats could be used to train an automatic classification algorithm (random
forest), and (b) to optimize acoustic analysis protocols by combining automatic classification
with visual post-validation, whereby we evaluated the proportion of sound files to be postvalidated
for different thresholds of classification accuracy. Classifiers were trained at species
or sonotype (group of species with similar calls) level. Random forest models confirmed the
reliability of using calls of both free-flying and hand-released bats to train custom-built
automatic classifiers. To achieve a general classification accuracy of ~85%, random forest
had to be trained with at least 500 pulses per species/sonotype. For seven out of 20 sonotypes,
the most abundant in our dataset, we obtained high classification accuracy (>90%). Adopting
a desired accuracy probability threshold of 95% for the random forest classifier, we found that
the percentage of sound files required for manual post-validation could be reduced by up to
75%, a significant saving in terms of workload. Combining automatic classification with
manual ID through fully customizable classifiers implemented in open-source software as
demonstrated here shows great potential to help overcome the acknowledged risks and biases
associated with the sole reliance on automatic classification.
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