02632nas a2200289 4500008004100000245011000041210006900151520174700220653001501967653002501982653002502007653000902032653001702041653002502058653001002083653001802093653001702111653002102128653002402149653001902173100001502192700001402207700001802221700001402239700002202253856006702275 2019 eng d00aAutomatic standardized processing and identification of tropical bat calls using deep learning approaches0 aAutomatic standardized processing and identification of tropical3 a

Consistent and comparable metrics to automatically monitor biodiversity across the landscape remain a gold-standard for biodiversity research, yet such approaches have frequently been limited to a very small selection of species for which visual approaches (e.g., camera traps) make continuous monitoring possible. Acoustic-based methods have been widely applied in the monitoring of bats and some other taxa across extended spatial scales, but are have yet to be applied to diverse tropical communities.

In this study, we developed a software program “Waveman” and prepared a reference library using over 880 audio-files from 36 Asian bat species. The software incorporated a novel network “BatNet” and a re-checking strategy (ReChk) to maximize accuracy. In Waveman, BatNet outperforms three other published networks: CNNFULL, VggNet and ResNet_v2, with over 90% overall accuracy and 0.94 AUC on the ROC plot. The classification accuracy rates for all 36 species are at least 86% when analysed in combination. Moreover, our library preparation and ReChk greatly improved the sensitivity and reduced the false positive rate, when tested with 15 species for which more detailed and situationally diverse records were available. Finally, BatNet was successfully used to identify Hipposideros larvatus and Rhinolophus siamensis from three different environments. We hope this pipeline is useful tool to process bioacoustic data accurately, effectively and automatically, therefore allowing for greater standardization and comparability for researchers to understand bat activities across space and time and therefore provide a consistent tool for monitoring biodiversity for management and conservation.

10aAlgorithms10aAutomated monitoring10aAutomatic processing10abats10abioacoustics10aBiodiversity metrics10aCalls10aDeep learning10aecholocation10amachine learning10aMonitoring protocol10aNeural network1 aChen, Xing1 aZhao, Jun1 aChen, Yan-hua1 aZhou, Wei1 aHughes, Alice, C. uhttps://linkinghub.elsevier.com/retrieve/pii/S000632071930896101291nas a2200169 4500008004100000245007000041210006900111520077400180653001700954653001800971653002000989100001601009700001601025700001901041700002001060856004101080 2019 eng d00aNew aspects in birdsong recognition utilizing the gabor transform0 aNew aspects in birdsong recognition utilizing the gabor transfor3 a

In this paper we will be concerned with mathematical methods for birdsong recognition and classification. Current approaches compute the spectrogram of an audio recording using the Gabor transform, which is then used as input for a convolutional neural network (CNN) to classify the recording. While recent work is dedicated to finding the best hyperparameters for training the CNN and for data augmentation, the parameters for the Gabor transform and the signal detection methods receive less attention. We aim to close this gap by evaluating the effect of different window lengths on the overall classification accuracy. Additionally we propose a method for denoising signals based on quantiles to account for different signal to noise ratios in the dataset.

10abioacoustics10aDeep learning10aGabor transform1 aHEUER, Sven1 aTAFO, Pavel1 aHOLZMANN, Hajo1 aDahlke, Stephan uhttps://bio.acousti.ca/it/node/5742001975nas a2200205 4500008004100000245009300041210006900134520129400203653001801497653003101515653003601546100002101582700001801603700001601621700002101637700002901658700002001687700002101707856004101728 2019 eng d00aApplying convolutional neural networks to the analysis of mouse ultrasonic vocalizations0 aApplying convolutional neural networks to the analysis of mouse 3 a

Many species in diverse taxonomic groups, including rodents, bats, and insects, communicate with complex ultrasonic vocalizations (USVs) (>20 kHz). Two main components of processing and analyzing USV recordings include detection and classification of syllable types. Recently we developed an efficient algorithm for detecting mouse USVs (Automatic Mouse Ultrasound Detector (A-MUD)). The main challenge is detecting USVs under conditions with a low signal-to-noise ratio, while minimizing rates of false positives (FP). Mice produce many short USVs (< 10 millisecond) that inflate FPs. We aimed to improve the detection of mouse USVs with A-MUD by classifying vocalizations into three discrete syllable types (with 0, 1, or ≥2 frequency-jumps) or FP. Supervised Convolutional Neural Networks (CNNs) were fed by 2D Gammatone Spectrograms (GSs) adapted to the frequency range of mice. Evaluation of performance shows that CNNs yielded an overall accuracy of 95±1.2% and macro-F1 score of 90±2.7%. In contrast, multilayer feed-forward neural networks fed by vectorized spectrograms provided an overall accuracy of only 85.4±1.9% and macro-F1 score of 75.4±2.9%, and therefore, the chosen CNNs outperformed this conventional classification method.

10aDeep learning10aImage-based classification10aUltrasonic vocalizations (USVs)1 aAbbasi, Reyhaneh1 aBalazs, Peter1 aNOLL, Anton1 aNICOLAKIS, Doris1 aMarconi, Maria, Adelaide1 aZala, Sarah, M.1 aPenn, Dustin, J. uhttps://bio.acousti.ca/it/node/5741901962nam 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/10.1007/978-3-030-27947-9