02615nas a2200301 4500008004100000245008200041210006900123260001600192300000900208490000600217520172400223653001501947653001201962653002801974653001002002653002202012653001202034653003202046653002202078653001502100653002102115100001902136700002902155700003002184700002802214700002702242856004402269 2017 eng d00aSpecies Richness (of Insects) Drives the Use of Acoustic Space in the Tropics0 aSpecies Richness of Insects Drives the Use of Acoustic Space in  cJan-11-2017 a10960 v93 a

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.

10aamphibians10aARBIMON10aBiodiversity monitoring10abirds10acommunity ecology10ainsects10apassive acoustic monitoring10arapid assessments10aSoundscape10aspecies richness1 aAide, Mitchell1 aHernández-Serna, Andres1 aCampos-Cerqueira, Marconi1 aAcevedo-Charry, Orlando1 aDeichmann, Jessica, L. uhttp://www.mdpi.com/2072-4292/9/11/109602624nas 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/