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/S1574954118301900