02673nas a2200229 4500008004100000245010500041210006900146520188000215653001702095653002102112653002502133653002602158653002102184653001802205653002402223653001902247100002602266700002502292700002302317700001702340856008602357 2019 eng d00aIndividual signatures outweigh social group identity in contact calls of a communally nesting parrot0 aIndividual signatures outweigh social group identity in contact 3 a

Despite longstanding interest in the evolutionary origins and maintenance of vocal learning, we know relatively little about how social dynamics influence vocal learning processes in natural populations. The “signaling group membership” hypothesis proposes that socially learned calls evolved and are maintained as signals of group membership. However, in fission–fusion societies, individuals can interact in social groups across various social scales. For learned calls to signal group membership over multiple social scales, they must contain information about group membership over each of these scales, a concept termed “hierarchical mapping.” Monk parakeets (Myiopsitta monachus), small parrots native to South America, exhibit vocal mimicry in captivity and fission–fusion social dynamics in the wild. We examined patterns of contact call acoustic similarity in Uruguay to test the hierarchical mapping assumption of the signaling group membership hypothesis. We also asked whether geographic variation patterns matched regional dialects or geographic clines that have been documented in other vocal learning species. We used visual inspection, spectrographic cross-correlation and random forests, a machine learning approach, to evaluate contact call similarity. We compared acoustic similarity across social scales and geographic distance using Mantel tests and spatial autocorrelation. We found high similarity within individuals, and low, albeit significant, similarity within groups at the pair, flock and site social scales. Patterns of acoustic similarity over geographic distance did not match mosaic or graded patterns expected in dialectal or clinal variation. Our findings suggest that monk parakeet social interactions rely more heavily upon individual recognition than group membership at higher social scales.

10acontact call10agroup membership10ahierarchical mapping10aindividual signatures10amachine learning10amonk parakeet10aMyiopsitta monachus10avocal learning1 aSmith-Vidaurre, Grace1 aAraya-Salas, Marcelo1 aWright, Timothy, F1 aNaguib, Marc uhttps://academic.oup.com/beheco/advance-article/doi/10.1093/beheco/arz202/568071502781nas a2200181 4500008004100000245010000041210006900141520217000210653002102380653001702401653002302418653002102441653002202462653002202484100001902506700001302525856006102538 2019 eng d00aSource separation in ecoacoustics: A roadmap towards versatile soundscape information retrieval0 aSource separation in ecoacoustics A roadmap towards versatile so3 a

A comprehensive assessment of ecosystem dynamics requires the monitoring of biological, physical and social changes. Changes that cannot be observed visually may be trackable acoustically through soundscape analysis. Soundscapes vary greatly depending on geophysical events, biodiversity and human activities. However, retrieving source‐specific information from geophony, biophony and anthropophony remains a challenging task, due to interference by simultaneous sound sources. Audio source separation is a technique that aims to recover individual sound sources when only mixtures are accessible. Here, we review techniques of monoaural audio source separation with the fundamental theories and assumptions behind them. Depending on the availability of prior information about the source signals, the task can be approached as a blind source separation or a model‐based source separation. Most blind source separation techniques depend on assumptions about the behaviour of the source signals, and their performance may deteriorate when the assumptions fail. Model‐based techniques generally do not require specific assumptions, and the models are directly learned from labelled data. With the recent advances of deep learning, the model‐based techniques can yield state‐of‐the‐art separation performance, accordingly facilitate content‐based audio information retrieval. Source separation techniques have been adopted in several ecoacoustic applications to evaluate the contributions from biodiversity and anthropogenic disturbance to soundscape dynamics. They can also be employed as nonlinear filters to improve the recognition of bioacoustic signals. To effectively retrieve ecological information from soundscapes, source separation is a crucial tool. We believe that the future integrations of ecological hypotheses and deep learning can realize a high‐performance source separation for ecoacoustics, and accordingly improve soundscape‐based ecosystem monitoring. Therefore, we outline a roadmap for applying source separation to assist in soundscape information retrieval and hope to promote cross‐disciplinary collaboration.

10aacoustic habitat10abiodiversity10aecosystem dynamics10amachine learning10apassive acoustics10aSignal processing1 aLin, Tzu‐Hao1 aTsao, Yu uhttps://onlinelibrary.wiley.com/doi/abs/10.1002/rse2.14103020nas a2200229 4500008004100000245009000041210006900131520224000200653002802440653001702468653004002485653002902525653002102554653002702575653005502602100001802657700001602675700001702691700001702708700001902725856004602744 2019 eng d00aImplementation of Artificial Intelligence for Classification of Frogs in Bioacoustics0 aImplementation of Artificial Intelligence for Classification of 3 a

This research presents the implementation of artificial intelligence (AI) for classification of frogs in symmetry of the bioacoustics spectral by using the feedforward neural network approach (FNNA) and support vector machine (SVM). Recently, the symmetry concept has been applied in physics, and in mathematics to help make mathematical models tractable to achieve the best learning performance. Owing to the symmetry of the bioacoustics spectral, feature extraction can be achieved by integrating the techniques of Mel-scale frequency cepstral coefficient (MFCC) and mentioned machine learning algorithms, such as SVM, neural network, and so on. At the beginning, the raw data information for our experiment is taken from a website which collects many kinds of frog sounds. This in fact saves us collecting the raw data by using a digital signal processing technique. The generally proposed system detects bioacoustic features by using the microphone sensor to record the sounds of different frogs. The data acquisition system uses an embedded controller and a dynamic signal module for making high-accuracy measurements. With regard to bioacoustic features, they are filtered through the MFCC algorithm. As the filtering process is finished, all values from ceptrum signals are collected to form the datasets. For classification and identification of frogs, we adopt the multi-layer FNNA algorithm in machine learning and the results are compared with those obtained by the SVM method at the same time. Additionally, two optimizer functions in neural network include: scaled conjugate gradient (SCG) and gradient descent adaptive learning rate (GDA). Both optimization methods are used to evaluate the classification results from the feature datasets in model training. Also, calculation results from the general central processing unit (CPU) and Nvidia graphics processing unit (GPU) processors are evaluated and discussed. The effectiveness of the experimental system on the filtered feature datasets is classified by using the FNNA and the SVM scheme. The expected experimental results of the identification with respect to different symmetry bioacoustic features of fifteen frogs are obtained and finally distinguished.

10aartificial intelligence10abioacoustics10afeedforward neural network approach10agraphics processing unit10amachine learning10asupport vector machine10asymmetry; Mel-scale frequency cepstral coefficient1 aChao, Kuo-Wei1 a-ZeHu, Nian1 aChao, Yi-Chu1 a-KaiSu, Chin1 aChiu, Wei-Hang uhttps://www.mdpi.com/2073-8994/11/12/145402632nas 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/S000632071930896101631nas a2200181 4500008004100000022001400041245009500055210006900150260001600219520103000235653002201265653001601287653002101303653002701324100002201351700002101373856005501394 2019 eng d a0044-843500aAutomated classification of bees and hornet using acoustic analysis of their flight sounds0 aAutomated classification of bees and hornet using acoustic analy cJul-01-20193 a

To investigate how to accurately identify bee species using their sounds, we conducted acoustic analysis to identify three pollinating bee species (Apis mellifera, Bombus ardens, Tetralonia nipponensis) and a hornet (Vespa simillima xanthoptera) by their flight sounds. Sounds of the insects and their environment (background noises and birdsong) were recorded in the field. The use of fundamental frequency and mel-frequency cepstral coefficients to describe feature values of the sounds, and supported vector machines to classify the sounds, correctly distinguished sound samples from environmental sounds with high recalls and precision (0.96–1.00). At the species level, our approach could classify the insect species with relatively high recalls and precisions (0.7–1.0). The flight sounds of V.s. xanthoptera, in particular, were perfectly identified (precision and recall 1.0). Our results suggest that insect flight sounds are potentially useful for detecting bees and quantifying their activity.

10aacoustic analysis10aHymenoptera10amachine learning10aspecies classification1 aKawakita, Satoshi1 aIchikawa, Kotaro uhttp://link.springer.com/10.1007/s13592-018-0619-6