Awareness: A parallel approach against noise

Abstract

Unlike other pollutants, many of the noise effects on people have a clear subjective component that go beyond the objective physiological effects that the physical phenomenon causes. Among them, annoyance is the most documented subjective response to noise, it being defined as a feeling of discomfort or displeasure that occurs when noise intrudes our everyday activities. For decades it has been one of the hot topics of environmental acoustics. But, while many studies have focused on the possible correlation between sound metrics and the adverse reaction of the population, the effect of non-acoustic factors on the annoyance response has recently emerged as a hot topic in international transport research groups. The aim of this paper is to present a novel approach to the assessment of noise impacts that could be of value to railway operations, complementing traditional approaches based on the reduction of exposure. Awareness comprises several non-acoustical factors pointed out in the past as affecting annoyance: information (accessibility, transparency and understanding), trust, influence/voice, attitude towards the source, predictability of the noise situation, awareness of negative effects… The project aims to obtain knowledge of this factor, analyse its influence, and take advantage of it to mitigate annoyance. This is an approach that has not been exploited in depth in the past, and does not imply the reduction of noise emissions at all, so that it can be managed together with other noise control measures. It is based on the hypothesis that “bringing information closer to residents will reduce noise annoyance”, and tries to answer the following question: How does Awareness Influence Noise Annoyance? To what extent can Communication or other Non-Acoustical measures modify Awareness in order to help residents to cope with Noise?

Publication
Proceedings of the 22nd International Congress on Sound and Vibration
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Luis Gasco
Luis Gasco
NLP Research Engineer

NLP, Information Extraction, NLU, LLM