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/7933047/02770nas 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<p>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 &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 &ldquo; unknown &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 &ldquo; unknown &rdquo; events . The advantage makes the proposed method more suitable for automated field recording analysis.</p>
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/S157495411630231X