02400nas a2200241 4500008004100000022001300041245007900054210006900133260001600202300001400218490000800232520145800240653002501698653002301723653004001746653002501786653002601811100002501837700002001862700001801882700002501900856023301925 2019 eng d a0003682X00aA real-time bird sound recognition system using a low-cost microcontroller0 arealtime bird sound recognition system using a lowcost microcont cJan-05-2019 a194 - 2010 v1483 a

Monitoring the environment using stand-alone sensor nodes provides valuable information to researchers, practitioners and policy makers. One such problem in this area is bird activity monitoring using its sound patterns. There are several existing devices that can record bird sounds in a region. Then, the stored data is processed off-line. However, the time between the acquisition and processing stages while using such devices is generally of the order of weeks. This approach is not amenable to real-time monitoring. Therefore, there is a need for a system which is able to both monitor the environment and process data on the system itself. The most important attribute of such a system is its power consumption since it operates in the environment only by its battery. In this study, we propose such a stand-alone, energy efficient, low-level, custom-made, real-time bird call processing system concentrated on single-labeled bird calls. The system is composed of a microphone, Texas Instruments Tiva C microcontroller, and a storage unit. The proposed system enables data recording, preliminary on-board signal processing, feature extraction, classification, and data storage. In the proposed system, we simultaneously record and process data. Hence, the system not only stores the environmental sounds, it also classifies the detected birds on-site. The proposed system offers flexibility (both in hardware and software) for expansion.

10aBird call processing10aFeature extraction10aMel frequency cepstrum coefficients10aReal-time processing10aSpectral noise gating1 aKüc̣üktopcu, Okan1 aMasazade, Engin1 aÜnsalan, Cem1 aVarshney, Pramod, K. uhttps://linkinghub.elsevier.com/retrieve/pii/S0003682X18304213https://api.elsevier.com/content/article/PII:S0003682X18304213?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0003682X18304213?httpAccept=text/plain00538nas a2200181 4500008004100000022001300041245005700054210005000111260001600161300001400177490000800191653001300199653001600212653002100228653002000249100002100269856006600290 2017 eng d a0003347200aBirdsong ‘performance’ studies: a sad commentary0 aBirdsong performance studies a sad commentary cJan-11-2017 a209 - 2100 v13310abirdsong10aperformance10asexual selection10avocal deviation1 aKroodsma, Donald uhttp://linkinghub.elsevier.com/retrieve/pii/S000334721730306802624nas a2200289 4500008004100000022001400041245011100055210006900166260001600235300001600251490000700267520172500274653002701999653002702026653001002053653002902063653002502092653001502117653002202132653001502154653002602169653001602195100002602211700002102237700002702258856004902285 2017 eng d a2329-929000aSpectrogram Enhancement Using Multiple Window Savitzky-Golay (MWSG) Filter for Robust Bird Sound Detection0 aSpectrogram Enhancement Using Multiple Window SavitzkyGolay MWSG cJan-06-2017 a1183 - 11920 v253 a

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.

10abioacoustic monitoring10abird sound detection10abirds10adirectional spectrograms10ahidden Markov models10amonitoring10anoise measurement10arobustness10aSavitzky-Golay filter10aspectrogram1 aKoluguri, Nithin, Rao1 aMeenakshi, Nisha1 aGhosh, Prasanta, Kumar uhttp://ieeexplore.ieee.org/document/7933047/02171nas a2200265 4500008004100000022001400041245008800055210006900143260001600212300001600228490000700244520143100251653001401682653001001696653001201706653002001718653001901738653001601757653002401773653001101797653001201808100001701820700001901837856004901856 2017 eng d a2329-929000aOn-Bird Sound Recordings: Automatic Acoustic Recognition of Activities and Contexts0 aOnBird Sound Recordings Automatic Acoustic Recognition of Activi cJan-06-2017 a1193 - 12060 v253 a

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'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.

10aacoustics10abirds10acontext10aevent detection10aimage analysis10amicrophones10apattern recognition10aspeech10azoology1 aStowell, Dan1 aGill, Lisa, F. uhttp://ieeexplore.ieee.org/document/7933044/02348nas a2200265 4500008004100000245008600041210006900127260001600196300001400212490000600226520155300232653001701785653002501802653001501827653001201842100001901854700002301873700002201896700002101918700002301939700002201962700002401984700002102008856005302029 2017 eng d00aMatlabHTK: a simple interface for bioacoustic analyses using hidden Markov models0 aMatlabHTK a simple interface for bioacoustic analyses using hidd cJan-05-2017 a615 - 6210 v83 a

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.

10abioacoustics10ahidden Markov models10amonitoring10aseabird1 aRanjard, Louis1 aReed, Benjamin, S.1 aLanders, Todd, J.1 aRayner, Matt, J.1 aFriesen, Megan, R.1 aSagar, Rachel, L.1 aDunphy, Brendon, J.1 aGaggiotti, Oscar uhttp://doi.wiley.com/10.1111/mee3.2017.8.issue-502770nas a2200253 4500008004100000022001300041245007800054210006900132260001600201520193400217653003902151653002502190653002702215653002702242653003902269100001502308700001902323700001702342700002802359700001802387700001902405700002602424856006602450 2017 eng d a1574954100aAutomated bird acoustic event detection and robust species classification0 aAutomated bird acoustic event detection and robust species class cJan-04-20173 a

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.

10aautomated acoustic event detection10aautoregressive model10abioacoustic monitoring10aGaussian mixture model10arobust bird species classification1 aZhao, Zhao1 aZhang, Sai-hua1 aXu, Zhi-yong1 aBellisario, Kristen, M.1 aDai, Nian-hua1 aOmrani, Hichem1 aPijanowski, Bryan, C. uhttp://linkinghub.elsevier.com/retrieve/pii/S157495411630231X02240nas 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/S0003682X14000024