Characterizing amplitude and frequency modulation cues in natural soundscapes: A pilot study on four habitats of a biosphere reserve

• DOI: 10.1121/10.0001174
• Publication Date: 2020-05-06T14:51:46Z
• Authors: Etienne Thoret; Léo Varnet; Yves Boubenec; Régis Férriere; François-Michel L...; Bernie Krause; Christian Lorenzi
• Keywords: Signal processing; [SCCO.NEUR] Cognitive science/Neuroscience; Bayes Theorem; Pilot Projects; 15. Life on land; 01 natural sciences; Amplitude modulation; [SDE.BE] Environmental Sciences/Biodiversity and Ecology; Birds; 03 medical and health sciences; Sound; 0302 clinical medicine; Speech sounds; Acoustic ecology; Machine learning; 0103 physical sciences; Animal communication; Auditory system; General procedures and instrumentation; Animals; Humans; Cues; [PHYS.MECA.ACOU] Physics [physics]/Mechanics [physics]/Acoustics [physics.class-ph]; Sound source perception; Ecosystem

Natural soundscapes correspond to the acoustical patterns produced by biological and geophysical sound sources at different spatial and temporal scales for a given habitat. This pilot study aims to characterize the temporal-modulation information available to humans when perceiving variations in soundscapes within and across natural habitats. This is addressed by processing soundscapes from a previous study [Krause, Gage, and Joo. (2011). Landscape Ecol. 26, 1247] via models of human auditory processing extracting modulation at the output of cochlear filters. The soundscapes represent combinations of elevation, animal, and vegetation diversity in four habitats of the biosphere reserve in the Sequoia National Park (Sierra Nevada, USA). Bayesian statistical analysis and support vector machine classifiers indicate that: (i) amplitude-modulation (AM) and frequency-modulation (FM) spectra distinguish the soundscapes associated with each habitat; and (ii) for each habitat, diurnal and seasonal variations are associated with salient changes in AM and FM cues at rates between about 1 and 100 Hz in the low (<0.5 kHz) and high (>1–3 kHz) audio-frequency range. Support vector machine classifications further indicate that soundscape variations can be classified accurately based on these perceptually inspired representations.