4.6 Article

Multilevel Annoyance Modelling of Short Environmental Sound Recordings

期刊

SUSTAINABILITY
卷 13, 期 11, 页码 -

出版社

MDPI
DOI: 10.3390/su13115779

关键词

noise; annoyance evaluation; citizen; perceptive test; smart-city; annoyance modelling; wireless acoustic sensor network

资金

  1. European Research Council (ERC) under the European Union [740696]
  2. Secretaria d'Universitats i Recerca from the Departament d'Empresa i Coneixement (Generalitat de Catalunya)
  3. Universitat Ramon Llull [2020-URL-Proj-054]

向作者/读者索取更多资源

This study utilizes short recordings from Wireless Acoustic Sensor Networks in Milan and conducts an online survey to categorize and rate environmental sound sources, finding that a psychoacoustic model can effectively explain the impact of sounds on individuals when considering different source types.
The recent development and deployment of Wireless Acoustic Sensor Networks (WASN) present new ways to address urban acoustic challenges in a smart city context. A focus on improving quality of life forms the core of smart-city design paradigms and cannot be limited to simply measuring objective environmental factors, but should also consider the perceptual, psychological and health impacts on citizens. This study therefore makes use of short (1-2.7 s) recordings sourced from a WASN in Milan which were grouped into various environmental sound source types and given an annoyance rating via an online survey with N=100 participants. A multilevel psychoacoustic model was found to achieve an overall R-2=0.64 which incorporates Sharpness as a fixed effect regardless of the sound source type and Roughness, Impulsiveness and Tonality as random effects whose coefficients vary depending on the sound source. These results present a promising step toward implementing an on-sensor annoyance model which incorporates psychoacoustic features and sound source type, and is ultimately not dependent on sound level.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据