@article {53351, title = {Stronger together: Combining automated classifiers with manual post-validation optimizes the workload vs reliability trade-off of species identification in bat acoustic surveys}, journal = {Ecological Informatics}, year = {2018}, month = {Jan-11-2018}, 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 analysing 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 \>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 post-validated 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.

}, keywords = {Amazon, bioacoustics, Chiroptera, echolocation, Machine-learning algorithms}, issn = {15749541}, doi = {10.1016/j.ecoinf.2018.11.004}, url = {https://linkinghub.elsevier.com/retrieve/pii/S1574954118300232https://api.elsevier.com/content/article/PII:S1574954118300232?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S1574954118300232?httpAccept=text/plain}, author = {L{\'o}pez-Baucells, Adri{\`a} and Torrent, Laura and Rocha, Ricardo and Paulo E.D. Bobrowiec and Jorge M. Palmeirim and Christoph F.J. Meyer} } @article {50905, title = {Frog sound identification using extended k-nearest neighbor classifier}, journal = {Journal of Physics: Conference Series}, volume = {890}, year = {2017}, month = {Jan-09-2017}, pages = {012070}, abstract = {

Frog sound identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed to evaluate the accuracy of frog sound identification. This paper presents a frog sound identification with Extended k-Nearest Neighbor (EKNN) classifier. The EKNN classifier integrates the nearest neighbors and mutual sharing of neighborhood concepts, with the aims of improving the classification performance. It makes a prediction based on who are the nearest neighbors of the testing sample and who consider the testing sample as their nearest neighbors. In order to evaluate the classification performance in frog sound identification, the EKNN classifier is compared with competing classifier, k -Nearest Neighbor (KNN), Fuzzy k -Nearest Neighbor (FKNN) k - General Nearest Neighbor (KGNN)and Mutual k -Nearest Neighbor (MKNN) on the recorded sounds of 15 frog species obtained in Malaysia forest. The recorded sounds have been segmented using Short Time Energy and Short Time Average Zero Crossing Rate (STE+STAZCR), sinusoidal modeling (SM), manual and the combination of Energy (E) and Zero Crossing Rate (ZCR) (E+ZCR) while the features are extracted by Mel Frequency Cepstrum Coefficient (MFCC). The experimental results have shown that the EKNCN classifier exhibits the best performance in terms of accuracy compared to the competing classifiers, KNN, FKNN, GKNN and MKNN for all cases.

}, issn = {1742-6588}, doi = {10.1088/1742-6596/890/1/012070}, url = {http://stacks.iop.org/1742-6596/890/i=1/a=012070?key=crossref.393dd3e387b6a34eb3971240d516f44b}, author = {Mukahar, Nordiana and Rosdi, Bakhtiar Affendi and Ramli, Dzati Athiar and Jaafar, Haryati} } @article {47314, title = {Automatic bird sound detection in long real-field recordings: Applications and tools}, journal = {Applied Acoustics}, volume = {80}, year = {2014}, month = {Jan-06-2014}, pages = {1 - 9}, abstract = {

The primary purpose for pursuing this research is to present a modular approach that enables reliable automatic bird species identification on the basis of their sound emissions in the field. A practical and complete computer-based framework is proposed to detect and time-stamp particular bird species in continuous real field recordings. Acoustic detection of avian sounds can be used for the automatized monitoring of multiple bird taxa and querying in long-term recordings for species of interest for research- ers, conservation practitioners, and decision makers, such as environmental indicator taxa and threa- tened species. This work describes two novel procedures and offers an open modular framework that detects and time-stamps online calls and songs of target bird species and is fast enough to report results in reasonable time for non-processed field recordings of many thousands files and is generic enough to accommodate any species. The framework is evaluated on two large corpora of real field data, targeting the calls and songs of American Robin Turdus migratorius , a Northamerican oscine passerine (true song- bird) and the Common Kingfisher Alcedo atthis , a non-passerine species with a wide distribution through- out Eurasia and North Africa. With the aim of promoting the widespread use of digital autonomous recording units (ARUs) and species recognition technologies the processing code and a large corpus of audio recordings is provided in order to enable other researchers to perform and assess comparative experiments.

}, keywords = {bird recognition, birdsong detection, computational ecology}, issn = {0003682X}, doi = {10.1016/j.apacoust.2014.01.001}, url = {http://linkinghub.elsevier.com/retrieve/pii/S0003682X14000024}, author = {Potamitis, Ilyas and Ntalampiras, Stavros and Olaf Jahn and Riede, Klaus} } @article {12075, title = {Speckled Bush Cricket Data Logger - Project Report}, year = {2015}, abstract = {

This report summarises work carried out on the Speckled Bush Cricket data logger project by Wil Bennett and Dave Chesmore at the University of York and Ed Baker at the Natural History Museum, who together designed and constructed - at the time of writing - a total of ten data logging devices to monitor the presence of Leptophyes punctatissima and other environmental variables such as temperature and humidity. This document summarises the capabilities and functionality of these devices, and comprehensively documents correct operational procedure for deploying the devices in the field.

}, doi = {10.6084/m9.figshare.1430094}, author = {Bennett, Wil and E. David Chesmore and Edward Baker} }