<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Koluguri, Nithin Rao</style></author><author><style face="normal" font="default" size="100%">Meenakshi, G. Nisha</style></author><author><style face="normal" font="default" size="100%">Ghosh, Prasanta Kumar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spectrogram Enhancement Using Multiple Window Savitzky-Golay (MWSG) Filter for Robust Bird Sound Detection</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE/ACM Transactions on Audio, Speech, and Language Processing</style></secondary-title><short-title><style face="normal" font="default" size="100%">IEEE/ACM Trans. Audio Speech Lang. Process.</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">bioacoustic monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">bird  sound  detection</style></keyword><keyword><style  face="normal" font="default" size="100%">birds</style></keyword><keyword><style  face="normal" font="default" size="100%">directional spectrograms</style></keyword><keyword><style  face="normal" font="default" size="100%">hidden Markov models</style></keyword><keyword><style  face="normal" font="default" size="100%">monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">noise measurement</style></keyword><keyword><style  face="normal" font="default" size="100%">robustness</style></keyword><keyword><style  face="normal" font="default" size="100%">Savitzky-Golay filter</style></keyword><keyword><style  face="normal" font="default" size="100%">spectrogram</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-06-2017</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/document/7933047/</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">1183 - 1192</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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&amp;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&amp;ndash;frequency (T&amp;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.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhao, Zhao</style></author><author><style face="normal" font="default" size="100%">Zhang, Sai-hua</style></author><author><style face="normal" font="default" size="100%">Xu, Zhi-yong</style></author><author><style face="normal" font="default" size="100%">Kristen M. Bellisario</style></author><author><style face="normal" font="default" size="100%">Dai, Nian-hua</style></author><author><style face="normal" font="default" size="100%">Omrani, Hichem</style></author><author><style face="normal" font="default" size="100%">Bryan C. Pijanowski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automated bird acoustic event detection and robust species classification</style></title><secondary-title><style face="normal" font="default" size="100%">Ecological Informatics</style></secondary-title><short-title><style face="normal" font="default" size="100%">Ecological Informatics</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">automated acoustic event detection</style></keyword><keyword><style  face="normal" font="default" size="100%">autoregressive model</style></keyword><keyword><style  face="normal" font="default" size="100%">bioacoustic monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">Gaussian mixture model</style></keyword><keyword><style  face="normal" font="default" size="100%">robust bird species classification</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-04-2017</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://linkinghub.elsevier.com/retrieve/pii/S157495411630231X</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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 &amp;rsquo; 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 &amp;ldquo; unknown &amp;rdquo; 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 &amp;ldquo; unknown &amp;rdquo; events . The advantage makes the proposed method more suitable for automated field recording analysis.&lt;/p&gt;
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