Source identification from unperceived low-frequency noise emissions at a Madrid home

被引：1

作者：

de Castro, Leon Jose Azcarate ^{[1
]}

Baeza-Moyano, David ^{[2
]}

Contreras, Gaston Sanglier ^{[1
]}

Gonzalez-Lezcano, Roberto Alonso ^{[1
]}

机构：

[1] CEU Univ, Univ San Pablo, Escuela Politecn Super, Dept Architecture & Design, Alcorcon 28668, Madrid, Spain

[2] CEU Univ, Univ San Pablo, Fac Pharm, Dept Chem & Biochem, Alcorcon 28668, Madrid, Spain

来源：

BUILDING AND ENVIRONMENT | 2024年 / 255卷

关键词：

Low frecuency waves; Public health; Acoustic waves; Acoustic energy; Levels of sound; INFRASOUND;

D O I：

10.1016/j.buildenv.2024.111440

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

People may be exposed to energy sources that they cannot perceive with their senses, but which may be harmful to their organism, and therefore, individuals cannot avoid them. One of these energy sources is the sound, particularly sound out of the hearing range (20 - 20000 Hz). Although the sounds are imperceptible for frequencies below 200 Hz unless they have high intensities. Sound with frequencies below 200 Hz is called " low frequency sound " . This study focuses on low frequency sound generated by artificial sources, and specially in sound located in urban areas. Specifically in the measurement and detection of low frequency sources from the perspective of individuals who are manifesting the symptoms associated with their exposure. To this end, a household of Madrid with individuals who have symptoms is taken as sample. This home did not have large potential sources of low-frequency sounds near its location, such as streets with high intensity of traffic or the subway in order to better contrast other possible sources that are not so obvious. The results show high levels of sound emission at the lowest frequency range (20 - 200 Hz). These results also show that filters should not be applied to remove non-audible frequency spectrums, such as A type, because it omits sounds in urban areas that could affect people. Data treatment incorporates analysis methods based on machine learning which allow differentiate between sources without measuring on them. Finally, further developments must incorporate measurements bellow 20 Hz and will increase the numbers of households sampled.

引用

页数：18

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