@article {53580, title = {Contributions of MIR to soundscape ecology. Part 2: Spectral timbral analysis for discriminating soundscape components}, journal = {Ecological Informatics}, year = {2019}, month = {Jan-02-2019}, abstract = {

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.

}, issn = {15749541}, doi = {10.1016/j.ecoinf.2019.01.008}, url = {https://linkinghub.elsevier.com/retrieve/pii/S1574954118301900}, author = {VanSchaik, Jack and Zhao, Zhao and Gasc, Amandine and Omrani, Hichem and Bryan C. Pijanowski} } @article {47352, title = {Automated bird acoustic event detection and robust species classification}, journal = {Ecological Informatics}, year = {2017}, month = {Jan-04-2017}, abstract = {

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.

}, keywords = {automated acoustic event detection, autoregressive model, bioacoustic monitoring, Gaussian mixture model, robust bird species classification}, issn = {15749541}, doi = {10.1016/j.ecoinf.2017.04.003}, url = {http://linkinghub.elsevier.com/retrieve/pii/S157495411630231X}, author = {Zhao, Zhao and Zhang, Sai-hua and Xu, Zhi-yong and Kristen M. Bellisario and Dai, Nian-hua and Omrani, Hichem and Bryan C. Pijanowski} }