城市森林声景观分类与识别(绿色开放空间达标研究)

Lailatul Inayah, Suyatno, S. Indrawati
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引用次数: 1

摘要

-城市森林是绿色开放空间的一部分,在城市地区具有审美和社会功能。城市森林与各种城市活动引起的噪音问题是分不开的。当然,这导致了城市森林整体功能的转变。一种声音组成,它与特定的景观相互作用,称为音景。本研究旨在基于声环境感知对城市森林中的声景观进行分类,并基于环境声学参数对城市森林中的声景观进行识别。在本研究中,使用Kano模型分析的声景框架参考ISO soundscape 12913-1-2014。在此框架下,使用客观参数和影响声景积极观点的因素来评估声景方法。用聚类层次聚类法对调查问卷进行声景分类。然后,通过测量目标声学参数对每个聚类进行识别。辨认工作也进行了声走。使用的参数为L10、L93、Lmax、LEq、τ1、Φ1和dr。根据研究结果,声景可分为交通声为主、自然声和人声为主、人声为主三大类。这可以通过几个预先确定的参数进行物理解释。根据研究结果,声景观可分为交通主导声、自然声和人声、人声三大类。Route上的声景识别结果表明,自然声(风声和鸟鸣)的表达值为Leq=57.24 dBA, L10=59.13 dBA, L93=54.52 dBA, Lmax=63.41 dBA, τ1=2.97 s, dan Φ1=0.3 DR=9.6 dB。此外,利用光谱图进行识别显示,风和鸟类在这条路线上的鸣叫占主导地位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification and Identification of Urban Forest Soundscapes (Study of the Reached Green Open Space Standard)
- An urban forest is part of green open space, which has aesthetic and social functions in an urban area. Urban forests cannot be separated from the problem of noise caused by diverse urban activities. Of course, this causes a shift in the function of urban forests as a whole. A sound composition that occurs and interacts with certain landscapes called the soundscapes. This study aims to classify the soundscapes in urban forests based on the perception of an acoustic environment and identify the soundscapes in urban forests based on environmental acoustic parameters. In this study, the soundscapes framework analyzed by using the Kano Model refers to ISO Soundscapes 12913-1-2014. In this framework, evaluate the soundscapes approach using objective parameters and factors that influence positive perspectives on the soundscapes. Classification of soundscapes done through questionnaires processed with Agglomerative Hierarchy Clustering. Then, identified each cluster by using the measurement of objective acoustic parameters. Identification also carried out the soundwalk. The parameters used are L10, L93, Lmax, LEq, τ1, Φ1, and DR. Based on the results of the study, the soundscapes can be classified into three groups, namely, the dominance of traffic, natural and human sounds, and human sounds. This can be explained physically through several predetermined parameters. Based on the results of the study, the soundscapes can be classified into three groups, namely, traffic dominance, natural and human sounds, and human sounds. The results of the soundscapes identification on Route show the natural sounds (wind and bird chirps) expressed by values Leq=57.24 dBA, L10=59.13 dBA, L93=54.52 dBA, Lmax=63.41 dBA, τ1=2.97 s, dan Φ1=0.3 DR=9.6 dB. Also, identification using a spectrogram shows the dominance of wind and birds chirping on this route.
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