01917nas a2200193 4500008004100000245011500041210006900156520126800225653002701493653001901520653003401539653001501573100001501588700001601603700001501619700002201634700002601656856004101682 2021 eng d00aDevelopment and test of a bat calls detection and classification method based on convolutional neural networks0 aDevelopment and test of a bat calls detection and classification3 a

Automated acoustic monitoring methods are frequently used to survey bat activity around wind turbines. The algorithms are often based on spectral features or threshold values of the recordings. Due to the generality of these features, a lot of recordings are noise, making manual analysis and labelling of the recordings time consuming. In this paper, we present an approach based on convolutional neural networks to detect and classify bat calls respectively. Recordings are converted to Mel-frequency cepstral coefficients (MFCCs), which are then fed as an image into the convolutional neural networks (CNNs) for classification. A dataset consisting of 43585 recordings gathered at 5 m height was used to train and test this method. An accuracy of 99.7% was achieved on a test set for the binary classification of noise and bat calls. For the species classification, this approach achieved an accuracy of 96%. Both networks, trained on data gathered at 5 m, were also tested on recordings gathered at heights of 33 m, 65 m and 95 m. In case of the binary classification task, the results showed an increased rate of misclassifications among noise recordings. For species classification, there was a higher amount of misclassifcations among all species.

10aBat echolocation calls10aclassification10aconvolutional neural networks10afield test1 aPaumen, Y.1 aMälzer, M.1 aAlipek, S.1 aMoll, Lüdtke, J.1 aSchauer-Weisshahn, H. uhttps://bio.acousti.ca/sv/node/5844101962nam a2200289 4500008004100000020002200041245006700063210006700130520111600197653001701313653001401330653001901344653001801363653001701381653000901398653002801407100002001435700002301455700002001478700002301498700002301521700001901544700001801563700001901581700001701600856005501617 2019 eng d a978-3-030-27946-200aDeep Representation Learning for Orca Call Type Classification0 aDeep Representation Learning for Orca Call Type Classification3 a

Marine mammals produce a wide variety of vocalizations. There is a growing need for robust automatic classification methods especially in noisy underwater environments in order to access large amounts of bioacoustic signals and to replace tedious and error prone human perceptual classification. In case of the northern resident killer whale (Orcinus orca), echolocation clicks, whistles, and pulsed calls make up its vocal repertoire. Pulsed calls are the most intensively studied type of vocalization. In this study we propose a hybrid call type classification approach outperforming our previous work on supervised call type classification consisting of two components: (1) deep representation learning of killer whale sounds by investigating various autoencoder architectures and data corpora and (2) subsequent supervised training of a ResNet18 call type classifier on a much smaller dataset by using the pre-trained representations. The best semi-supervised trained classification model achieved a test accuracy of 96% and a mean test accuracy of 94% outperforming our previous work by 7% points.

10abioacoustics10acall type10aclassification10aDeep learning10aKiller whale10aOrca10aRepresentation learning1 aEkštein, Kamil1 aBergler, Christian1 aSchmitt, Manuel1 aCheng, Rachael, Xi1 aSchröter, Hendrik1 aMaier, Andreas1 aBarth, Volker1 aWeber, Michael1 aNöth, Elmar uhttp://link.springer.com/10.1007/978-3-030-27947-902344nas a2200229 4500008004100000022001400041245016000055210006900215260001600284300001400300490000700314520158400321653002301905653001901928653001201947653001901959653001301978653001701991100002002008700003102028856005502059 2016 eng d a1435-944800aThe vocal repertoire of the domesticated zebra finch: a data-driven approach to decipher the information-bearing acoustic features of communication signals0 avocal repertoire of the domesticated zebra finch a datadriven ap cJan-03-2016 a285 - 3150 v193 a

Although a universal code for the acoustic features of animal vocal communication calls may not exist, the thorough analysis of the distinctive acoustical features of vocalization categories is important not only to decipher the acoustical code for a specific species but also to understand the evolution of communication signals and the mechanisms used to produce and understand them. Here, we recorded more than 8000 examples of almost all the vocalizations of the domesticated zebra finch, Taeniopygia guttata: vocalizations produced to establish contact, to form and maintain pair bonds, to sound an alarm, to communicate distress or to advertise hunger or aggressive intents. We characterized each vocalization type using complete representations that avoided any a priori assumptions on the acoustic code, as well as classical bioacoustics measures that could provide more intuitive interpretations. We then used these acoustical features to rigorously determine the potential information-bearing acoustical features for each vocalization type using both a novel regularized classifier and an unsupervised clustering algorithm. Vocalization categories are discriminated by the shape of their frequency spectrum and by their pitch saliency (noisy to tonal vocalizations) but not particularly by their fundamental frequency. Notably, the spectral shape of zebra finch vocalizations contains peaks or formants that vary systematically across categories and that would be generated by active control of both the vocal organ (source) and the upper vocal tract (filter).

10aAcoustic signature10aclassification10aMeaning10aRegularization10aSongbird10avocalization1 aElie, Julie, E.1 aTheunissen, Frédéric, E. uhttp://link.springer.com/10.1007/s10071-015-0933-602101nas a2200265 4500008004100000245012000041210006900161260001600230520128800246653001401534653001701548653001901565653001501584653001301599653002401612653001301636100002001649700002201669700002601691700002201717700002001739700001701759700001601776856004301792 2016 eng d00aAcoustic classification of dolphins in the California Current using whistles, echolocation clicks, and burst pulses0 aAcoustic classification of dolphins in the California Current us cJan-12-20163 a

Passive acoustic monitoring of dolphins is limited by our ability to classify calls to species. Significant overlap in call characteristics among many species, combined with a wide range of call types and acoustic behavior, makes classification of calls to species challenging. Here, we introduce BANTER, a compound acoustic classification method for dolphins that utilizes information from all call types produced by dolphins rather than a single call type, as has been typical for acoustic classifiers. Output from the passive acoustic monitoring software, PAMGuard, was used to create independent classifiers for whistles, echolocation clicks, and burst pulses, which were then merged into a final, compound classifier for each species. Classifiers for five species found in the California Current ecosystem were trained and tested using 153 single-species acoustic events recorded during a 4.5 mo combined visual and acoustic shipboard cetacean survey off the west coast of the United States. Correct classification scores for individual species ranged from 71% to 92%, with an overall correct classification score of 84% for all five species. The conceptual framework of this approach easily lends itself to other species and study areas as well as to noncetacean taxa.

10aacoustics10aburst pulses10aclassification10adelphinids10adolphins10aecholocation clicks10awhistles1 aRankin, Shannon1 aArcher, Frederick1 aKeating, Jennifer, L.1 aOswald, Julie, N.1 aOswald, Michael1 aCurtis, Alex1 aBarlow, Jay uhttp://doi.wiley.com/10.1111/mms.1238103205nas a2200217 4500008004100000245008000041210006900121300001200190490000700202520252100209653001902730653001802749653001902767653001502786653001702801653001502818653001702833100001702850700001302867856010702880 1996 eng d00aElytro-tergal stridulation in weevils (Insecta: Coleoptera: Curculionoidea)0 aElytrotergal stridulation in weevils Insecta Coleoptera Curculio a703-7730 v303 aMany Curculionidae, Scolytidae and Platypodidae stridulate by rubbing a plectrum on tergite VII against a subapical file on the elytral underside. The file on the right elytron may be smaller than that on the left, or absent; on either elytron it usually comprises a series of parallel ridges. The plectrum generally consists of one or more pair of setiferous tubercles, although other structures are sometimes employed. This stridulatory system (‘type 1’) is found in members of 14 curculionid subfamilies and in Scolytidae. Platypodidae have a similar and probably homologous organ, although the morphology of the file differs somewhat from that found elsewhere and the plectrum is a simple flange. Within Curculionidae three other elytro-tergal stridulatory systems have been found. Firstly, in some Cossoninae and Molytinae, the file is a row of tubercles on tergite VII and the plectrum is a ridge on the elytron (‘type 2’); this is newly reported here. Secondly, females of Ithyporini, some Camptorhinini and some Cryptorhynchini have part of the wing binding patch on tergite VII modified to act as a file, with a plectrum on the elytra (‘type 3’). Thirdly, some Amycterinae have a file of ridges on tergite VII and a plectrum on the elytra (‘type 4’), apparently an autapomorphy of a restricted group of species. In addition, some Curculionidae have a plectrum on the hind femur and a file on the abdominal sternum (some Cryptorhynchinae) or elytron (some Otidocephalinae). Scolytidae have at least three other stridulatory organs: two on the head and one involving the elytra and sternum VIII. Some systematic conclusions are drawn: the clear homology of the type 1 structures in Scolytidae and the majority of the stridulating Curculionidae indicates that the Curculionidae is paraphyletic with respect to the Scolytidae; Scolytidae should therefore be treated as a subfamily of Curculionidae. The structure of the platypodid file and plectrum provide equivocal information and do not aid resolution of the phylogenetic placement of this family. Monophyly of some Ithyporini and Camptorhinini is suggested by the distribution of type 3, but there is insufficient evidence to demonstrate paraphyly of the Cryptorhynchini. The distribution of type 2 does not indicate monophyly of the Cossoninae and Molytinae since the structures in the two are almost certainly homoplastic. All known records of elytro-tergal stridulatory organs in the Scolytidae, Platypodidae and Curculionidae are listed.10aclassification10aCurculionidae10aCurculionoidea10amorphology10aPlatypodidae10aScolytidae10astridulation1 aLyal, C.H.C.1 aKing, T. uhttps://bio.acousti.ca/sv/content/elytro-tergal-stridulation-weevils-insecta-coleoptera-curculionoidea