@article {53057, title = {Species Richness (of Insects) Drives the Use of Acoustic Space in the Tropics}, journal = {Remote Sensing}, volume = {9}, year = {2017}, month = {Jan-11-2017}, pages = {1096}, abstract = {

Acoustic ecology, or ecoacoustics, is a growing field that uses sound as a tool to evaluate animal communities. In this manuscript, we evaluate recordings from eight tropical forest sites that vary in species richness, from a relatively low diversity Caribbean forest to a megadiverse Amazonian forest, with the goal of understanding the relationship between acoustic space use (ASU) and species diversity across different taxonomic groups. For each site, we determined the acoustic morphospecies richness and composition of the biophony, and we used a global biodiversity dataset to estimate the regional richness of birds. Here, we demonstrate how detailed information on activity patterns of the acoustic community (\<22 kHz) can easily be visualized and ASU determined by aggregating recordings collected over relatively short periods (4\–13 days). We show a strong positive relationship between ASU and regional and acoustic morphospecies richness. Premontane forest sites had the highest ASU and the highest species richness, while dry forest and montane sites had lower ASU and lower species richness. Furthermore, we show that insect richness was the best predictor of variation in total ASU, and that insect richness was proportionally greater at high-diversity sites. In addition, insects used a broad range of frequencies, including high frequencies (\>8000 Hz), which contributed to greater ASU. This novel approach for analyzing the presence and acoustic activity of multiple taxonomic groups contributes to our understanding of ecological community dynamics and provides a useful tool for monitoring species in the context of restoration ecology, climate change and conservation biology.

}, keywords = {amphibians, ARBIMON, Biodiversity monitoring, birds, community ecology, insects, passive acoustic monitoring, rapid assessments, Soundscape, species richness}, doi = {10.3390/rs9111096}, url = {http://www.mdpi.com/2072-4292/9/11/1096}, author = {T. Mitchell Aide and Hern{\'a}ndez-Serna, Andres and Campos-Cerqueira, Marconi and Acevedo-Charry, Orlando and Jessica L. Deichmann} } @article {52674, title = {Automatic identification of rainfall in acoustic recordings}, journal = {Ecological Indicators}, volume = {75}, year = {2017}, month = {Jan-04-2017}, pages = {95 - 100}, abstract = {

The rainfall regime is one of the main abiotic components that can cause modifications in the breeding activity of animal species. It has a direct effect on the environmental conditions, and acts as a modifier of the landscape and soundscape. Variations in water quality and acidity, flooding, erosion, and sound distortion are usually related with the presence of rain. Thereby, ecological studies in populations and communities would benefit from improvements in the estimation of rainfall patterns throughout space and time.

In this paper, a method for automatic detection of rainfall in forests by using acoustic recordings is proposed. This approach is based on the estimation of the mean value and signal to noise ratio of the power spectral density in the frequency band in which the sound of the raindrops falling over the vegetation layers of the forest is more prominent (i.e. 600\–1200 Hz). The results of this method were compared with human auditory identification and data provided by a pluviometer. We achieved a correlation of 95.23\% between the data provided by the pluviometer and the predictions of a regression model. Furthermore, we attained a general accuracy between 92.90\% and 99.98\% when identifying different intensity levels of rainfall on recordings.

Nowadays, passive monitoring recorders have been extensively used to study of acoustic-based breeding processes of several animal species. Our method uses the signals acquired by these recorders in order to identify and quantify rainfall events in short and long time spans. The proposed approach will automatically provide information about the rainfall patterns experienced by target species based on audio recordings.

}, keywords = {bioacoustics, Environmental monitoring, Precipitation measuremen, Rain detection, Soundscape ecology}, issn = {1470160X}, doi = {10.1016/j.ecolind.2016.12.018}, url = {http://linkinghub.elsevier.com/retrieve/pii/S1470160X16307117}, author = {Bedoya, Carol and Isaza, Claudia and Daza, Juan M. and L{\'o}pez, Jos{\'e} D.} } @article {52577, title = {Birdsong {\textquoteleft}performance{\textquoteright} studies: a sad commentary}, journal = {Animal Behaviour}, volume = {133}, year = {2017}, month = {Jan-11-2017}, pages = {209 - 210}, keywords = {birdsong, performance, sexual selection, vocal deviation}, issn = {00033472}, doi = {10.1016/j.anbehav.2017.09.019}, url = {http://linkinghub.elsevier.com/retrieve/pii/S0003347217303068}, author = {Kroodsma, Donald} } @article {51243, title = {Passive acoustic monitoring in ecology and conservation}, number = {1(2)}, year = {2017}, institution = {WWF-UK}, address = {Woking, United Kingdom}, author = {Ella Browning and Rory Gibb and Paul Glover-Kapfer and Kate Elizabeth Jones} } @article {51242, title = {"My naturesound" - nature observations with sound recordings}, journal = {Biodiversity Data Journal}, volume = {5}, year = {2017}, month = {Aug-10-2018}, pages = {e20200}, abstract = {

The dataset consists observation data of birds, amphibians and insects by citizen scientists with on site audio recordings. The dataset gives the possibility to analyze the suitablility of mobile devices for recording animal vocalizations and their use in reporting.

}, issn = {1314-2836}, doi = {10.3897/BDJ.5.e20200}, url = {https://bdj.pensoft.net/articles.php?id=20200}, author = {Runnel, Veljo and Peterson, Marko and Zirk, Allan} } @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 {50814, title = {Bioacoustics or pitfall traps: comparison of a modern and traditional method to estimate Ensifera richness}, journal = {CURRENT SCIENCE}, volume = {113}, year = {2017}, pages = {561}, author = {Manisha Tomar and Abhay Pratap Singh and Swati Diwakar} } @mastersthesis {50813, title = {WASIS - Bioacoustic Species Identication based on Multiple Feature Extraction and Classication Algorithms}, year = {2017}, school = {Universidade Estadual de Campinas}, abstract = {

Automatic identication of animal species based on their sounds is one of the means to conduct research in bioacoustics. This research domain provides, for instance, ways to monitor rare and endangered species, to analyze changes in ecological communities, or ways to study the social meaning of animal calls in their behavioral contexts. Identi cation mechanisms are typically executed in two stages: feature extraction and classi cation. Both stages present challenges, in computer science and in bioacoustics. The choice of e ective feature extraction and classi cation algorithms is a challenge on any audio recognition system, especially in bioacoustics. Considering the wide variety of animal groups studied, algorithms are tailored to speci c groups. Audio classi cation techniques are also sensitive to the extracted features, and conditions surrounding the recordings. As a results, most bioacoustic softwares are not extensible, therefore limiting the kinds of recognition experiments that can be conducted. Given this scenario, this dissertation proposes a software architecture that allows multiple feature extraction, feature fusion and classi cation algorithms to support scientists and the general public on the identi cation of animal species through their recorded sounds. This architecture was implemented by the WASIS software, freely available on the Web. Since WASIS is open-source and expansible, experts can perform experiments with many combinations of pairs descriptor-classi er to choose the most appropriate ones for the identi cation of given animal sub-groups. A number of algorithms were implemented, serving as the basis for a comparative study that recommends sets of feature extraction and classi cation algorithms for three animal groups.

}, author = {Leandro Tacioli} } @article {47718, title = {Spectrogram Enhancement Using Multiple Window Savitzky-Golay (MWSG) Filter for Robust Bird Sound Detection}, journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing}, volume = {25}, year = {2017}, month = {Jan-06-2017}, pages = {1183 - 1192}, abstract = {

Bird sound detection from real-field recordings is essential for identifying bird species in bioacoustic monitoring. Variations in the recording devices, environmental conditions, and the presence of vocalizations from other animals make the bird sound detection very challenging. In order to overcome these challenges, we propose an unsupervised algorithm comprising two main stages. In the first stage, a spectrogram enhancement technique is proposed using a multiple window Savitzky\–Golay (MWSG) filter. We show that the spectrogram estimate using MWSG filter is unbiased and has lower variance compared with its single window counterpart. It is known that bird sounds are highly structured in the time\–frequency (T\–F) plane. We exploit these cues of prominence of T-F activity in specific directions from the enhanced spectrogram, in the second stage of the proposed method, for bird sound detection. In this regard, we use a set of four moving average filters that when applied to the enhanced spectrogram, yield directional spectrograms that capture the direction specific information. We propose a thresholding scheme on the time varying energy profile computed from each of these directional spectrograms to obtain frame-level binary decisions of bird sound activity. These individual decisions are then combined to obtain the final decision. Experiments are performed with three different datasets, with varying recording and noise conditions. Frame level F-score is used as the evaluation metric for bird sound detection. We find that the proposed method, on average, achieves higher F-score (10.24\% relative) compared to the best of the six baseline schemes considered in this work.

}, keywords = {bioacoustic monitoring, bird sound detection, birds, directional spectrograms, hidden Markov models, monitoring, noise measurement, robustness, Savitzky-Golay filter, spectrogram}, issn = {2329-9290}, doi = {10.1109/TASLP.2017.2690562}, url = {http://ieeexplore.ieee.org/document/7933047/}, author = {Koluguri, Nithin Rao and Meenakshi, G. Nisha and Ghosh, Prasanta Kumar} } @article {47717, title = {On-Bird Sound Recordings: Automatic Acoustic Recognition of Activities and Contexts}, journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing}, volume = {25}, year = {2017}, month = {Jan-06-2017}, pages = {1193 - 1206}, abstract = {

We introduce a novel approach to studying animal behavior and the context in which it occurs, through the use of microphone backpacks carried on the backs of individual free-flying birds. These sensors are increasingly used by animal behavior researchers to study individual vocalizations of freely behaving animals, even in the field. However, such devices may record more than an animal\&$\#$39;s vocal behavior, and have the potential to be used for investigating specific activities (movement) and context (background) within which vocalizations occur. To facilitate this approach, we investigate the automatic annotation of such recordings through two different sound scene analysis paradigms: A scene-classification method using feature learning, and an event-detection method using probabilistic latent component analysis. We analyze recordings made with Eurasian jackdaws (Corvus monedula) in both captive and field settings. Results are comparable with the state of the art in sound scene analysis; we find that the current recognition quality level enables scalable automatic annotation of audio logger data, given partial annotation, but also find that individual differences between animals and/or their backpacks limit the generalization from one individual to another. we consider the interrelation of \“scenes\” and \“events\” in this particular task, and issues of temporal resolution.

}, keywords = {acoustics, birds, context, event detection, image analysis, microphones, pattern recognition, speech, zoology}, issn = {2329-9290}, doi = {10.1109/TASLP.2017.2690565}, url = {http://ieeexplore.ieee.org/document/7933044/}, author = {Stowell, Dan and Gill, Lisa F.} } @article {47699, title = {MatlabHTK: a simple interface for bioacoustic analyses using hidden Markov models}, journal = {Methods in Ecology and Evolution}, volume = {8}, year = {2017}, month = {Jan-05-2017}, pages = {615 - 621}, abstract = {

1.Passive bioacoustic recording devices are now widely available and able to continuously record remotely located sites for extended periods, offering great potential for wildlife monitoring and management. Analysis of the huge data sets generated, in particular for specific biotic sound recognition, remains a critical bottleneck for widespread adoption of these technologies as current methods are labour intensive.

2. Several methods borrowed from speech processing frameworks, such as hidden Markov models, have been successful in analysing bioacoustic data, but the software implementations can be expensive and difficult to use for non-specialists involved in wildlife conservation. To remedy this, we present a software interface to a popular speech recognition system making it possible for non-experts to implement hidden Markov models for bioacoustic signal processing. Octave/Matlab functions are used to simplify the set-up and the definition of a bioacoustic signal recogniser as well as the analysis of the results.

3. We present the different functions as a workflow. To demonstrate how the package can be used, we give the results of an analysis of a bioacoustic monitoring data set to detect the nocturnal presence and behaviour of a cryptic seabird species, the common diving petrel Pelecanoides urinatrix urinatrix, from Northern New Zealand.


4. We show that the package MatlabHTK can be used efficiently to reconstruct the daily patterns of colony activity in the common diving petrel.

}, keywords = {bioacoustics, hidden Markov models, monitoring, seabird}, doi = {10.1111/mee3.2017.8.issue-510.1111/2041-210X.12688}, url = {http://doi.wiley.com/10.1111/mee3.2017.8.issue-5}, author = {Ranjard, Louis and Reed, Benjamin S. and Landers, Todd J. and Rayner, Matt J. and Friesen, Megan R. and Sagar, Rachel L. and Dunphy, Brendon J.}, editor = {Gaggiotti, Oscar} } @article {47624, title = {Non-invasive biophysical measurement of travelling waves in the insect inner ear}, journal = {Royal Society Open Science}, volume = {4}, year = {2017}, month = {Jul-18-2017}, pages = {170-171}, abstract = {

Frequency analysis in the mammalian cochlea depends on the propagation of frequency information in the form of a travelling wave (TW) across tonotopically arranged auditory sensilla. TWs have been directly observed in the basilar papilla of birds and the ears of bush-crickets (Insecta: Orthoptera) and have also been indirectly inferred in the hearing organs of some reptiles and frogs. Existing experimental approaches to measure TW function in tetrapods and bush-crickets are inherently invasive, compromising the fine-scale mechanics of each system. Located in the forelegs, the bush-cricket ear exhibits outer, middle and inner components; the inner ear containing tonotopically arranged auditory sensilla within a fluid-filled cavity, and externally protected by the leg cuticle. Here, we report bush-crickets with transparent ear cuticles as potential model species for direct, non-invasive measuring of TWs and tonotopy. Using laser Doppler vibrometry and spectroscopy, we show that increased transmittance of light through the ear cuticle allows for effective non-invasive measurements of TWs and frequency mapping. More transparent cuticles allow several properties of TWs to be precisely recovered and measured in vivo from intact specimens. Our approach provides an innovative, non-invasive alternative to measure the natural motion of the sensilla-bearing surface embedded in the intact inner ear fluid.

}, doi = {10.1098/rsos.170171}, url = {http://rsos.royalsocietypublishing.org/lookup/doi/10.1098/rsos.170171https://syndication.highwire.org/content/doi/10.1098/rsos.170171}, author = {Sarria-S, Fabio A. and Soulsbury, Carl D. and Fernando Montealegre-Zapata} } @article {47352, title = {Automated bird acoustic event detection and robust species classification}, journal = {Ecological Informatics}, year = {2017}, month = {Jan-04-2017}, abstract = {

N on - invasive b ioacoustic monitoring is becoming increasingly popular for b iodiversity conservation. Two automated methods for acoustic classification of bird species currently used are frame - based methods, a model that uses Hidden Markov Models (HMMs), and event - based methods, a model consisting of descriptive measurements or restricted to tonal or harmonic vocalizations. In this work, we propose a new method for automated field recording anal ysis with improved automated segm entation and robust bird species classification . We used a Gaussian Mixture Model ( GMM ) - based frame selection with an event - energy - based sifting procedure that selected representative acoustic events . We employed a Mel , band - pass filter bank on each event \’ s spectrogram. T he output in each subband was parameterized by an autoregressive (AR) model , which result ed in a feature consisting of all model coefficients. Finally, a support vector machine (SVM) algorithm was used for classification . The significance o f the proposed method lies in the parameterized feature s depict ing the species - specific spectral pattern . This experiment used a control audio dataset and real - world audio dataset comprised of field recordings of eleven bird species from the X eno - canto A rc hive , consisting of 2762 bird acoustic events with 339 detected \“ unknown \” events (corresponding to noise or unknown species vocalization s) . Compared with other recent approach es , our proposed method provide s comparable identification performance with respe ct to the eleven species of interest . Meanwhile, superior robu stness in real - world scenarios is achieved, which is expressed as the considerable improvement from 0.632 to 0.928 for the F - score metric regarding the \“ unknown \” events . The advantage makes the proposed method more suitable for automated field recording analysis.

}, keywords = {automated acoustic event detection, autoregressive model, bioacoustic monitoring, Gaussian mixture model, robust bird species classification}, issn = {15749541}, doi = {10.1016/j.ecoinf.2017.04.003}, url = {http://linkinghub.elsevier.com/retrieve/pii/S157495411630231X}, author = {Zhao, Zhao and Zhang, Sai-hua and Xu, Zhi-yong and Kristen M. Bellisario and Dai, Nian-hua and Omrani, Hichem and Bryan C. Pijanowski} } @article {47328, title = {Habitat- and matrix-related differences in species diversity and trait richness of vascular plants, Orthoptera and Lepidoptera in an urban landscape}, journal = {Urban Ecosystems}, year = {2017}, month = {Jan-04-2018}, abstract = {Urban growth is considered to be a major driver of environmental change. Urbanisation can affect urban biodiversity in different ways. So far, most studies focused on the impact of urbanisation on single taxa in one habitat type. In this study, we used data of species inventories and GIS-based landscape elements to examine the effects of habitat size and landscape composition on the species diversity of three taxonomic groups (vascular plants, Orthoptera and Lepidoptera) in meadows and ruderal sites in the urban region of Basel, Switzerland. We also related the responses of three species traits (body size, dispersal ability and food specialisation) to habitat size in Orthoptera and Lepidoptera. We found that species of the different taxonomic groups differed in their response to habitat size and landscape composition both in meadows and ruderal sites depending on the traits examined. The species richness of Orthoptera and Lepidoptera was positively related to meadow size but not to the size of ruderal sites, while the opposite was true for plants. For Lepidoptera in ruderal sites, the percentage cover of ruderal area in the closer surroundings was a better predictor of species richness than habitat size per se. To sustain high levels of urban biodiversity, we recommend that urban planners develop adequate management strategies to satisfy the different requirements of various taxonomic groups and to increase the quality of green sites surrounding the target habitat.}, keywords = {Butterflies, grasshoppers, plant functional richness, species{\textendash}area relationship, urbanisation}, issn = {1083-8155}, doi = {10.1007/s11252-017-0662-5}, url = {http://link.springer.com/10.1007/s11252-017-0662-5}, author = {Melliger, Ramona Laila and Rusterholz, Hans-Peter and Baur, Bruno} } @article {47327, title = {The use of bioacoustics in anuran taxonomy: theory, terminology, methods and recommendations for best practice}, journal = {Zootaxa}, volume = {4251}, year = {2017}, month = {Nov-04-2017}, pages = {1}, abstract = {

Vocalizations of anuran amphibians have received much attention in studies of behavioral ecology and physiology, but also provide informative characters for identifying and delimiting species. We here review the terminology and variation of frog calls from a perspective of integrative taxonomy, and provide hands-on protocols for recording, analyzing, comparing, interpreting and describing these sounds. Our focus is on advertisement calls, which serve as premating isolation mechanisms and, therefore, convey important taxonomic information. We provide recommendations for terminology of frog vocalizations, with call, note and pulse being the fundamental subunits to be used in descriptions and comparisons. However, due to the complexity and diversity of these signals, an unequivocal application of the terms call and note can be challenging. We therefore provide two coherent concepts that either follow a note-centered approach (defining uninterrupted units of sound as notes, and their entirety as call) or a call-centered approach (defining uninterrupted units as call whenever they are separated by long silent intervals) in terminology. Based on surveys of literature, we show that numerous call traits can be highly variable within and between individuals of one species. Despite idiosyncrasies of species and higher taxa, the duration of calls or notes, pulse rate within notes, and number of pulses per note appear to be more static within individuals and somewhat less affected by temperature. Therefore, these variables might often be preferable as taxonomic characters over call rate or note rate, which are heavily influenced by various factors. Dominant frequency is also comparatively static and only weakly affected by temperature, but depends strongly on body size. As with other taxonomic characters, strong call divergence is typically indicative of species-level differences, whereas call similarities of two populations are no evidence for them being conspecific. Taxonomic conclusions can especially be drawn when the general advertisement call structure of two candidate species is radically different and qualitative call differences are thus observed. On the other hand, quantitative differences in call traits might substantially vary within and among conspecific populations, and require careful evaluation and analysis. We provide guidelines for the taxonomic interpretation of advertisement call differences in sympatric and allopatric situations, and emphasize the need for an integrative use of multiple datasets (bio-acoustics, morphology, genetics), particularly for allopatric scenarios. We show that small-sized frogs often emit calls with frequency components in the ultrasound spectrum, although it is unlikely that these high frequencies are of biological relevance for the majority of them, and we illustrate that detection of upper harmonics depends also on recording distance because higher frequencies are attenuated more strongly. Bioacoustics remains a prime approach in integrative taxonomy of anurans if uncertainty due to possible intraspecific variation and technical artifacts is adequately considered and acknowledged.

}, keywords = {Amphibia, Anura, call, call analysis, call description, call variation, definitions, note, pulse, sound, species delimitation, taxonomy, vocalization}, issn = {1175-5326}, doi = {10.11646/zootaxa.4251.110.11646/zootaxa.4251.1.1}, url = {http://biotaxa.org/Zootaxa/issue/view/zootaxa.4251.1}, author = {J{\"o}rn K{\"o}hler and Martin Jansen and Ariel Rodr{\'\i}guez and Philippe J. R. Kok and Lu{\'\i}s Felipe Toledo and Emmrich, Mike and Frank Glaw and C{\'e}lio F. B. Haddad and Mark-Oliver R{\"o}del and Miguel Vences} } @article {47292, title = {Testing the performances of automated identification of bat echolocation calls: A request for prudence}, journal = {Ecological Indicators}, volume = {78}, year = {2017}, month = {Jan-07-2017}, pages = {416 - 420}, abstract = {

Echolocating bats are surveyed and studied acoustically with bat detectors routinely and worldwide, yet identification of species from calls often remains ambiguous or impossible due to intraspecific call variation and/or interspecific overlap in call design. To overcome such difficulties and to reduce workload, automated classifiers of echolocation calls have become popular, but their performance has not been tested sufficiently in the field. We examined the absolute performance of two commercially available programs (SonoChiro and Kaleidoscope) and one freeware package (BatClassify). We recorded noise from rain and calls of seven common bat species with Pettersson real-time full spectrum detectors in Sweden. The programs could always (100\%) distinguish rain from bat calls, usually (68\–100\%) identify bats to group ( Nyctalus/Vespertilio/Eptesicus, Pipistrellus, Myotis, Plecotus, Barbastella ) and usually (83\–99\%) recognize typical calls of some species whose echolocation pulses are structurally distinct ( Pipistrellus pygmaeus , Barbastella barbastellus ). Species with less characteristic echolocation calls were not identified reliably, including Vespertilio murinus (16\–26\%), Myotis spp. (4\–93\%) and Plecotus auritus (0\–89\%). All programs showed major although different shortcomings and the often poor performance raising serious concerns about the use of automated classifiers for identification to species level in research and surveys. We highlight the importance of validating output from automated classifiers, and restricting their use to specific situations where identification can be made with high confidence. For comparison we also present the result of a manual identification test on a random subset of the files used to test the programs. It showed a higher classification success but performances were still low for more problematic taxa.

}, keywords = {Biosonar, methodology, software, species identification, ultrasound}, issn = {1470160X}, doi = {10.1016/j.ecolind.2017.03.023}, url = {http://linkinghub.elsevier.com/retrieve/pii/S1470160X17301401}, author = {Rydell, Jens and Nyman, Stefan and Ekl{\"o}f, Johan and Jones, Gareth and Russo, Danilo} } @article {16462, title = {Two-stage detection of north Atlantic right whale upcalls using local binary patterns and machine learning algorithms}, journal = {Applied Acoustics}, volume = {120}, year = {2017}, month = {Jan-05-2017}, pages = {158 - 166}, abstract = {

In this paper, we investigate the effectiveness of two-stage classification strategies in detecting north Atlantic right whale upcalls. Time-frequency measurements of data from passive acoustic monitoring devices are evaluated as images. Vocalization spectrograms are preprocessed for noise reduction and tone removal. First stage of the algorithm eliminates non-upcalls by an energy detection algorithm. In the second stage, two sets of features are extracted from the remaining signals using contour-based and texture based methods. The former is based on extraction of time\–frequency features from upcall contours, and the latter employs a Local Binary Pattern operator to extract distinguishing texture features of the upcalls. Subsequently evaluation phase is carried out by using several classifiers to assess the effectiveness of both the contour-based and texture-based features for upcall detection. Comparing ROC curves of machine learning algorithms obtained from Cornell University\’s dataset reveals that LBP features improved performance accuracy up to 43\% over time\–frequency features. Classifiers such as the Linear Discriminant Analysis, Support Vector Machine, and TreeBagger achieve highest upcall detection rates with LBP features.

}, issn = {0003682X}, doi = {10.1016/j.apacoust.2017.01.025}, url = {http://linkinghub.elsevier.com/retrieve/pii/S0003682X17300774}, author = {Esfahanian, Mahdi and Erdol, Nurgun and Gerstein, Edmund and Zhuang, Hanqi} } @article {16409, title = {Potential for coupling the monitoring of bush-crickets with established large-scale acoustic monitoring of bats}, journal = {Methods in Ecology and Evolution}, year = {2017}, month = {Jan-01-2017}, abstract = {

Monitoring biodiversity over large spatial and temporal scales is crucial for assessing the impact of global changes and environmental mitigation measures. However, large-scale monitoring of invertebrates remains poorly developed despite the importance of these organisms in ecosystem functioning. Exciting possibilities applicable to professional and citizen science are offered by new recording techniques and methods of semi-automated species recognition based on sound detection.


Static broad-spectrum detectors deployed to record throughout whole nights have been recommended for standardised acoustic monitoring of bats, but they have the potential to also collect acoustic data for other species groups. Large-scale deployment of such systems is only viable when combined with robust automated species identification algorithms. Here we examine the potential of such a system for detecting, identifying and monitoring bush-crickets (Orthoptera of the family Tettigoniidae). We use incidental sound recordings generated by an extensive citizen science bat survey and recordings from intensive site surveys to test a semi-automated step-wise method with a classifier for assigning species identities. We assess species\’ diel activity patterns to make recommendations for survey timing and interpretation of existing nocturnal data sets and consider the feasibility of determining site occupancy.

Of six species of bush-crickets, the species classifier achieved over 85\% accuracy for three, speckled bush-cricket, dark bush-cricket and Roesel\&$\#$39;s bush-cricket. It should be possible to automatically scan recordings for these species with minimal manual validation. Further refinement of the classifier is required for the three remaining species, in particular for the acoustically similar short-winged conehead and long-winged conehead. Diel activity patterns are species specific and it may be necessary to adjust the hours over which the detectors record to increase detection of key species, but this must be weighed against the costs in terms of increased memory and battery use and equipment security during daytime.

We conclude that with logistical support and centralised semi-automated species identification it is now possible for the public to contribute to large-scale acoustic monitoring of Orthoptera while recording bats. Further innovation of sound classifier algorithms is needed and would be aided by improved reference sound libraries from multiple locations spanning species\’ ranges.

}, doi = {10.1111/2041-210X.12720}, url = {http://doi.wiley.com/10.1111/2041-210X.12720}, author = {Newson, Stuart E. and Bas, Yves and Murray, Ash and Gillings, Simon} }