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Bellisario K. M., VanSchaik J., Zhao Z., Gasc Amandine, Omrani H., Pijanowski B. C. (2019). Contributions of MIR to Soundscape Ecology. Part 2 : Spectral timbral analysis for discriminating soundscape components. Ecological Informatics, 51, 1-14. ISSN 1574-9541

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Lien direct chez l'éditeur doi:10.1016/j.ecoinf.2019.01.008

Titre
Contributions of MIR to Soundscape Ecology. Part 2 : Spectral timbral analysis for discriminating soundscape components
Année de publication2019
Type de documentArticle référencé dans le Web of Science WOS:000467661300001
AuteursBellisario K. M., VanSchaik J., Zhao Z., Gasc Amandine, Omrani H., Pijanowski B. C.
SourceEcological Informatics, 2019, 51, p. 1-14. ISSN 1574-9541
RésuméSoundscape 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.
Plan de classementEtudes, transformation, conservation du milieu naturel [082] ; Informatique [122] ; Sciences fondamentales / Techniques d'analyse et de recherche [020]
LocalisationFonds IRD [F B010075702]
Identifiant IRDfdi:010075702
Lien permanenthttp://www.documentation.ird.fr/hor/fdi:010075702

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