Short-term annoyance model of combined aircraft and road traffic noise based on partial loudness model

Chun, Chanil; Gwak, Doo Young; Yoon, Ki Youl; Lee, Soogab

doi:10.1007/s12206-018-0706-7

Cited by 2 publications

(2 citation statements)

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“…The masking effect can be quantified because it calculates the partial loudness according to the target and masker sound. The same concept was used in our previous study as well [26]. However, research on the annoyance of residents has not been conducted.…”

Section: Introductionmentioning

confidence: 99%

“…Kim et al executed the laboratory test to compare monaural and binaural listening to show that annoyance from the single and dummy head results in different tendencies with increasing noise level yet not as twice [27]. For this reason, in our previous study, a dummy head was used for binaural recording [26]. However, loudness was calculated with a model based on the assumption that loudness is summed across the ear [28].…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Mapping of Combined Noise Annoyance for Aircraft and Road Traffic Based on a Partial Loudness Model

Lee

Chun

Kim

et al. 2021

IJERPH

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Complex transportation systems often produce combined exposure to aircraft and road noise. Depending on the noise source, the annoyance response is different, and a masking effect occurs between the noise sources within the combined noise. Considering these characteristics, partial loudness was adopted to evaluate noise annoyance. First, a partial loudness model incorporating binaural inhibition was proposed and validated. Second, short- and long-term annoyance models were developed using partial loudness. Finally, the annoyance of combined noise was visualized as a map. These models can evaluate the annoyance by considering both the intensity and frequency characteristics of the noise. In addition, it is possible to quantify the masking effect that occurs between noise sources. Combined noise annoyance maps depict the degree of annoyance of residents and show the background noise effect, which is not seen on general noise maps.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%