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主要研究了多噪声源共同作用下的混合噪声烦恼度的评价过程与预测方法. 首先, 设计并完成了固定播放时长噪声样本作用下的烦恼度主观评价实验, 获得了人工合成的混合噪声样本作用下的混合噪声烦恼度(亦称总烦恼度) T 评价数据与构成混合噪声样本的所有单一噪声样本单独作用时的烦恼度i (i=1, 2, 3, , K; K为混合噪声样本中单一噪声样本的总数) 评价数据. 随后, 细致分析了两组评价数据之间的关系, 提出在已知i 的基础上利用多元线性回归模型预测T. 最后, 解决了如何确定模型中对应各i的权值i (i=1, 2, 3, , K) 的问题. 研究表明, 以所提出的权值确定方法建立的多元线性回归预测模型 能够较为成功地预测混合噪声样本作用下的总烦恼度评价值.In this paper, noise annoyance from a mixture of multiple single sources is studied with emphasis on subjective evaluation and objective prediction. From 10 subjects, annoyance values for all single and artificially combined noise samples are collected using the semantic differential method with a suitable verbal scale. We propose a novel method to determine the utility weights of a multivariate linear regression model by comparing the total annoyance T of the combined noise sample to every single annoyance i from its componential single sound sample. This method predicts T on the premise of given i. Our results demonstrate that the multivariate linear regression model and the calculated utility weights provide a good and conceptually simple framework to predict the total noise annoyance.
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