인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Previous studies suggested that introducing fountain sound could mitigate the discomfort and memory disturbance caused by structure borne sound from a metro, and proposed the prediction models for the discomfort after mitigation. However, these studies failed to identify the primary, secondary and significant influencing factors on the discomfort after mitigation, which hindered the proposal of optimal masking strategy and undermined the scientific validity of models. Additionally, previous analyses overlooked the primary, secondary and significant influencing factors on the memory disturbance after mitigation and lacked prediction model for it. Therefore, this study explored these aspects further. Based on auditory experiments, using partial least squares model and prediction model, this study found that considering total impact degree, the discomfort was predominantly influenced by the subjective loudness. However, the sound levels were the most important factors in determining the memory disturbance. The signal-to-noise ratio significantly influenced the discomfort but had no significant impact on the memory disturbance. Moreover, the subjective loudness emerged as the most effective predictor of the discomfort. While predicting the memory disturbance predominantly depended on the sound levels, and among the prediction models based on the sound levels, the predictive effectiveness of the energy summation model was comparable to that of the independent effects model. Furthermore, as global equivalent A-weighted sound level increased, the mitigation effect on discomfort became more evident, but its effectiveness in mitigating the memory disturbance gradually decreased. These conclusions could provide optimal strategies for enhancing such masking effects, and more effective prediction tools for such effects.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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