声景生态学数据分析与应用

Abstract

PDF HTML阅读 XML下载 导出引用 引用提醒 声景生态学数据分析与应用 DOI: 10.5846/stxb202110202956 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 中国科学院战略性先导科技专项（A类）（XDA23030401）；福建省科技计划项目（2021Y0071）；中国科学院青年促进会项目（2017351） Data analysis and application of soundscape ecology Author: Affiliation: Fund Project: The Strategic Priority Research Program of the Chinese Academy of Sciences [Grant No. XDA23030401]; The Fujian Science and Technology Projects [Grant No.2021Y0071]; The Youth Innovation Promotion Association, Chinese Academy of Sciences [Grant No. 2017351] 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:声景生态学是研究景观中生物与非生物声音在多种时空尺度下的声学格局与过程，揭示声音与人类以及声音与自然之间关系的学科。基于声景生态学的研究内容，从声景元素解析、生物多样性评估及人类身心健康评价应用案例中，梳理了数据分析的前沿方法。结论表明，分析技术的发展，特别是人工智能技术的进步，使声景生态学的研究呈现从人工到机器、从单一特征计算到多维特征提取、从单学科研究到多学科联合分析的技术化发展趋势，不断拓展着声景生态学的研究深度与广度。同时这些分析技术的发展也急需优化和标准化，来提高方法的通用性和研究结果间的可比性。此外，需要融合生态学、计算机科学和心理学等交叉学科的理论和方法，进一步推动声景生态学数据分析技术方法体系的完善。 Abstract:Soundscape ecology is an emerging discipline created by Pijanowski in 2011. It extensively absorbs the knowledge of landscape ecology, biogeography, psychoacoustics, bioacoustics and acoustic ecology, and regards them as its intellectual foundations. As such a comprehensive discipline, soundscape ecology studies the acoustic pattern and process of biological and abiotic sound from a landscape across variously spatial and temporal scales, revealing the relationships and interactions between human beings, nature, and sounds. With advances of sensor technology, popularization of 5G network, and development of edge computing technology, soundscape acquisition and monitoring approaches have been widely applied to disclose the insights of ecosystem, which will simultaneously produce a large amount of soundscape data and sequentially promote the appearance of series of soundscape data analysis methods. Therefore, there is an urgent need to summarize the analysis methods and application of soundscape data. Based on the research content of soundscape ecology, this paper describes the cutting—edge data analysis methods from the soundscape elements identification, biodiversity assessment to human physical and mental health evaluation. The results indicate that, with the development of analysis technologies, especially the advancement of artificial intelligence technologies, researches of soundscape ecology has presented the tendency of technicalization. To be specific, the story of data analysis technologies of soundscape ecology is experiencing a state from manual labor to machine learning, from single feature calculation to multi-dimensional feature extraction, from single-disciplinary research to multi-disciplinary conjoint analysis. It is constantly expanding the research depth and breadth of soundscape ecology. Meanwhile, these analysis technologies urgently need to be optimized and standardized, improving the adaptability of methods and the comparability of research results. Besides, the multi-disciplinary theories and methods from ecology, computer science, and psychology should be integrated, improving the data analysis technical system of soundscape ecology. 参考文献 相似文献 引证文献