03117nas a2200241 4500008004100000022001300041245018100054210006900235260001600304520208500320653001102405653001702416653001502433653001702448653003202465100002802497700001902525700001902544700002702563700002502590700002702615856023302642 2018 eng d a1574954100aStronger together: Combining automated classifiers with manual post-validation optimizes the workload vs reliability trade-off of species identification in bat acoustic surveys0 aStronger together Combining automated classifiers with manual po cJan-11-20183 a

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.

10aAmazon10abioacoustics10aChiroptera10aecholocation10aMachine-learning algorithms1 aLópez-Baucells, Adrià1 aTorrent, Laura1 aRocha, Ricardo1 aBobrowiec, Paulo, E.D.1 aPalmeirim, Jorge, M.1 aMeyer, Christoph, F.J. uhttps://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/plain02141nas a2200181 4500008004100000022001400041245008200055210006900137260001600206300001100222490000800233520152300241100002201764700002901786700002501815700002001840856009901860 2017 eng d a1742-658800aFrog sound identification using extended k-nearest neighbor classifier0 aFrog sound identification using extended ikinearest neighbor cla cJan-09-2017 a0120700 v8903 a

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.

1 aMukahar, Nordiana1 aRosdi, Bakhtiar, Affendi1 aRamli, Dzati, Athiar1 aJaafar, Haryati uhttp://stacks.iop.org/1742-6596/890/i=1/a=012070?key=crossref.393dd3e387b6a34eb3971240d516f44b02240nas a2200217 4500008004100000022001300041245008900054210006900143260001600212300001000228490000700238520156300245653002101808653002301829653002601852100002101878700002501899700001501924700001701939856006601956 2014 eng d a0003682X00aAutomatic bird sound detection in long real-field recordings: Applications and tools0 aAutomatic bird sound detection in long realfield recordings Appl cJan-06-2014 a1 - 90 v803 a

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.

10abird recognition10abirdsong detection10acomputational ecology1 aPotamitis, Ilyas1 aNtalampiras, Stavros1 aJahn, Olaf1 aRiede, Klaus uhttp://linkinghub.elsevier.com/retrieve/pii/S0003682X1400002401020nas a2200121 4500008004100000245005500041210005300096520060400149100001700753700002300770700001800793856008700811 2015 eng d00aSpeckled Bush Cricket Data Logger - Project Report0 aSpeckled Bush Cricket Data Logger Project Report3 a

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.

1 aBennett, Wil1 aChesmore, E. David1 aBaker, Edward uhttps://bio.acousti.ca/nl/content/speckled-bush-cricket-data-logger-project-report