Thoret Etienne, Varnet Léo, Boubenec Yves, Férriere Régis, Le Tourneau François-Michel, Krause Bernie, Lorenzi Christian
Laboratoire des systèmes perceptifs, UMR CNRS 8248, Département d'Etudes Cognitives, École normale supérieure, Université Paris Sciences et Lettres, 29 rue d'Ulm Paris, 75005, France.
Institut de Biologie de l'Ecole Normale Supérieure (IBENS), Université Paris Sciences et Lettres, CNRS, INSERM Paris, 75005, France.
J Acoust Soc Am. 2020 May;147(5):3260. doi: 10.1121/10.0001174.
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.
自然声景对应于给定栖息地中生物和地球物理声源在不同空间和时间尺度上产生的声学模式。这项初步研究旨在描述人类在感知自然栖息地内部和之间的声景变化时可获得的时间调制信息。这是通过处理先前一项研究[克劳斯、盖奇和朱。（2011年）。景观生态学。26，1247]中的声景来实现的，该研究通过人类听觉处理模型在耳蜗滤波器的输出端提取调制。这些声景代表了美国红杉国家公园（内华达山脉）生物圈保护区四个栖息地中海拔、动物和植被多样性的组合。贝叶斯统计分析和支持向量机分类器表明：（i）幅度调制（AM）和频率调制（FM）频谱区分了与每个栖息地相关的声景；（ii）对于每个栖息地，昼夜和季节变化与低（<0.5千赫）和高（>1 - 3千赫）音频范围内约1至100赫兹速率的AM和FM线索的显著变化相关。支持向量机分类进一步表明，基于这些受感知启发的表示，可以准确地对声景变化进行分类。
