Please use this identifier to cite or link to this item: http://hdl.handle.net/10637/15702

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

Title: Source identification from unperceived low-frequency noise emissions at a Madrid home
Authors : Azcárate de Castro, León José
Baeza Moyano, David
Sanglier Contreras, Gastón
González Lezcano, Roberto Alonso.
Keywords: Low frecuency wavesPublic healthAcoustic wavesAcoustic energyLevels of sound
Publisher: Elsevier
Citation: León José Azcárate de Castro, David Baeza-Moyano, Gastón Sanglier Contreras, Roberto Alonso González-Lezcano, Source identification from unperceived low-frequency noise emissions at a Madrid home, Building and Environment, Volume 255, 2024, 111440, ISSN 0360-1323, https://doi.org/10.1016/j.buildenv.2024.111440
Abstract: 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.
URI: http://hdl.handle.net/10637/15702
Rights : http://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
Open Access
ISSN: 0360-1323
Supported by: Acuerdo Transformativo - 2024
Issue Date: 19-Mar-2024
Center : Universidad San Pablo-CEU
Appears in Collections:Escuela de Politécnica Superior





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