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