02624nas 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<p>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&ndash;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&ndash;frequency (T&ndash;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.</p>
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<p>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 &ldquo;scenes&rdquo; and &ldquo;events&rdquo; in this particular task, and issues of temporal resolution.</p>
10aacoustics10abirds10acontext10aevent detection10aimage analysis10amicrophones10apattern recognition10aspeech10azoology1 aStowell, Dan1 aGill, Lisa, F.  uhttp://ieeexplore.ieee.org/document/7933044/