从众包音频数据中探索新兴声景特征

IF 7.1 1区 地球科学 Q1 ENVIRONMENTAL STUDIES Computers Environment and Urban Systems Pub Date : 2024-04-08 DOI:10.1016/j.compenvurbsys.2024.102112
Aura Kaarivuo , Jonas Oppenländer , Tommi Kärkkäinen , Tommi Mikkonen
{"title":"从众包音频数据中探索新兴声景特征","authors":"Aura Kaarivuo ,&nbsp;Jonas Oppenländer ,&nbsp;Tommi Kärkkäinen ,&nbsp;Tommi Mikkonen","doi":"10.1016/j.compenvurbsys.2024.102112","DOIUrl":null,"url":null,"abstract":"<div><p>The key component of designing sustainable, enriching, and inclusive cities is public participation. The soundscape is an integral part of an immersive environment in cities, and it should be considered as a resource that creates the acoustic image for an urban environment. For urban planning professionals, this requires an understanding of the constituents of citizens' emergent soundscape experience. The goal of this study is to present a systematic method for analyzing crowdsensed soundscape data with unsupervised machine learning methods. This study applies a crowdsensed sound- scape experience data collection method with low threshold for participation. The aim is to analyze the data using unsupervised machine learning methods to give insights into soundscape perception and quality.</p><p>For this purpose, qualitative and raw audio data were collected from 111 participants in Helsinki, Finland, and then clustered and further analyzed. We conclude that a machine learning analysis combined with accessible, mobile crowdsensing methods enable results that can be applied to track hidden experiential phenomena in the urban soundscape.</p></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":null,"pages":null},"PeriodicalIF":7.1000,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0198971524000413/pdfft?md5=cd512c7aaeca07125b7aafa5779034ba&pid=1-s2.0-S0198971524000413-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Exploring emergent soundscape profiles from crowdsourced audio data\",\"authors\":\"Aura Kaarivuo ,&nbsp;Jonas Oppenländer ,&nbsp;Tommi Kärkkäinen ,&nbsp;Tommi Mikkonen\",\"doi\":\"10.1016/j.compenvurbsys.2024.102112\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The key component of designing sustainable, enriching, and inclusive cities is public participation. The soundscape is an integral part of an immersive environment in cities, and it should be considered as a resource that creates the acoustic image for an urban environment. For urban planning professionals, this requires an understanding of the constituents of citizens' emergent soundscape experience. The goal of this study is to present a systematic method for analyzing crowdsensed soundscape data with unsupervised machine learning methods. This study applies a crowdsensed sound- scape experience data collection method with low threshold for participation. The aim is to analyze the data using unsupervised machine learning methods to give insights into soundscape perception and quality.</p><p>For this purpose, qualitative and raw audio data were collected from 111 participants in Helsinki, Finland, and then clustered and further analyzed. We conclude that a machine learning analysis combined with accessible, mobile crowdsensing methods enable results that can be applied to track hidden experiential phenomena in the urban soundscape.</p></div>\",\"PeriodicalId\":48241,\"journal\":{\"name\":\"Computers Environment and Urban Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0198971524000413/pdfft?md5=cd512c7aaeca07125b7aafa5779034ba&pid=1-s2.0-S0198971524000413-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers Environment and Urban Systems\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0198971524000413\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers Environment and Urban Systems","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0198971524000413","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
引用次数: 0

摘要

设计可持续、丰富和包容性城市的关键要素是公众参与。声景是城市沉浸式环境不可或缺的一部分,应将其视为创造城市环境声学形象的资源。对于城市规划专业人员来说,这就需要了解市民的声音景观体验的构成要素。本研究的目标是提出一种利用无监督机器学习方法分析人群声景数据的系统方法。本研究采用了一种参与门槛较低的众包声景体验数据收集方法。为此,我们在芬兰赫尔辛基收集了 111 名参与者的定性和原始音频数据,然后对其进行聚类和进一步分析。我们的结论是,将机器学习分析与便捷的移动人群感应方法结合起来,可以得出用于追踪城市声景中隐藏的体验现象的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Exploring emergent soundscape profiles from crowdsourced audio data

The key component of designing sustainable, enriching, and inclusive cities is public participation. The soundscape is an integral part of an immersive environment in cities, and it should be considered as a resource that creates the acoustic image for an urban environment. For urban planning professionals, this requires an understanding of the constituents of citizens' emergent soundscape experience. The goal of this study is to present a systematic method for analyzing crowdsensed soundscape data with unsupervised machine learning methods. This study applies a crowdsensed sound- scape experience data collection method with low threshold for participation. The aim is to analyze the data using unsupervised machine learning methods to give insights into soundscape perception and quality.

For this purpose, qualitative and raw audio data were collected from 111 participants in Helsinki, Finland, and then clustered and further analyzed. We conclude that a machine learning analysis combined with accessible, mobile crowdsensing methods enable results that can be applied to track hidden experiential phenomena in the urban soundscape.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
13.30
自引率
7.40%
发文量
111
审稿时长
32 days
期刊介绍: Computers, Environment and Urban Systemsis an interdisciplinary journal publishing cutting-edge and innovative computer-based research on environmental and urban systems, that privileges the geospatial perspective. The journal welcomes original high quality scholarship of a theoretical, applied or technological nature, and provides a stimulating presentation of perspectives, research developments, overviews of important new technologies and uses of major computational, information-based, and visualization innovations. Applied and theoretical contributions demonstrate the scope of computer-based analysis fostering a better understanding of environmental and urban systems, their spatial scope and their dynamics.
期刊最新文献
Spatiotemporal dynamics of ethnoracial diversity and segregation in Los Angeles County: Insights from mobile phone data Machine-based understanding of noise perception in urban environments using mobility-based sensing data Inclusive accessibility: Analyzing socio-economic disparities in perceived accessibility A graph-based modelling approach for the representation and analysis of urban conflicts Satisfying transport needs with low carbon emissions: Exploring individual, social, and built environmental factors
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1