02274nas a2200169 4500008004100000022001300041245012300054210006900177260001600246520167600262100002001938700001501958700001901973700001901992700002602011856006702037 2019 eng d a1574954100aContributions of MIR to soundscape ecology. Part 2: Spectral timbral analysis for discriminating soundscape components0 aContributions of MIR to soundscape ecology Part 2 Spectral timbr cJan-02-20193 a

Soundscape ecology evaluates biodiversity and environmental disturbances by investigating the interaction among soundscape components (biological, geophysical, and human-produced sounds) using data collected with autonomous recording units. Current analyses consider the acoustic properties of frequency and amplitude resulting in varied metrics, but rarely focus on the discrimination of soundscape components. Computational musicologists analyze similar data but consider a third acoustic property, timbre.

Here, we investigated the effectiveness of spectral timbral analysis to distinguish among dominant soundscape components. This process included manually labeling and extracting spectral timbral features for each recording. Then, we tested classification accuracy with linear and quadratic discriminant analyses on combinations of spectral timbral features.

Different spectral timbral feature groups distinguished between biological, geophysical, and manmade sounds in a single field recording. Furthermore, as we tested different combinations of spectral timbral features that resulted in both high and very low accuracy results, we found that they could be ordered to “sift” out field recordings by individual dominant soundscape component.

By using timbre as a new acoustic property in soundscape analyses, we could classify dominant soundscape components effectively. We propose further investigation into a sifting scheme that may allow researchers to focus on more specific research questions such as understanding changes in biodiversity, discriminating by taxonomic class, or to inspect weather-related events.

1 aVanSchaik, Jack1 aZhao, Zhao1 aGasc, Amandine1 aOmrani, Hichem1 aPijanowski, Bryan, C. uhttps://linkinghub.elsevier.com/retrieve/pii/S157495411830190002770nas 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

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 ’ 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 “ unknown ” 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 “ unknown ” events . The advantage makes the proposed method more suitable for automated field recording analysis.

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