Stronger together : combining automated classifiers with manual post-validation optimizes the workload vs reliability trade-off of species identification in bat acoustic surveys

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.

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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.

Item Type: Article
Schools: Schools > School of Environment and Life Sciences > Ecosystems and Environment Research Centre
Journal or Publication Title: Ecological Informatics
Publisher: Elsevier
ISSN: 1574-9541
Related URLs:
Funders: Foundation for Science and Technology (FCT), Portugal, Foundation for Research Support of the State of Amazonas, Brazil, Bat Conservation International, USA
Depositing User: Dr Christoph Meyer
Date Deposited: 30 Nov 2018 12:57
Last Modified: 15 Dec 2018 18:26
URI: http://usir.salford.ac.uk/id/eprint/49098

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