Institute of Space and Earth Information Science, Fok Ying Tung Remote Sensing Science Building, The Chinese University of Hong Kong, Hong Kong.
Institute of Space and Earth Information Science, Fok Ying Tung Remote Sensing Science Building, The Chinese University of Hong Kong, Hong Kong; Department of Geography and Resource Management, Wong Foo Yuan Building, The Chinese University of Hong Kong, Hong Kong.
Sci Total Environ. 2024 Nov 25;953:176083. doi: 10.1016/j.scitotenv.2024.176083. Epub 2024 Sep 10.
When investigating the relationship between the acoustic environment and human wellbeing, there is a potential problem resulting from data source self-correlation. To address this data source self-correlation problem, we proposed a third-party assessment combined with an artificial intelligence (TPA-AI) model. The TPA-AI utilized acoustic spectrograms to assess the soundscape's affective quality. First, we collected data on public perceptions of urban sounds (i.e., inviting 100 volunteers to label the affective quality of 7051 10-s audios on a polar scale from annoying to pleasant). Second, we converted the labeled audios to acoustic spectrograms and used deep learning methods to train the TPA-AI model, achieving a 92.88 % predictive accuracy for binary classification. Third, geographic ecological momentary assessment (GEMA) was used to log momentary audios from 180 participants in their daily life context, and we employed the well-trained TPA-AI model to predict the affective quality of these momentary audios. Lastly, we compared the explanatory power of the three methods (i.e., sound level meters, sound questionnaires, and the TPA-AI model) when estimating the relationship between momentary stress level and the acoustic environment. Our results indicate that the TPA-AI's explanatory power outperformed the sound level meter, while using a sound questionnaire might overestimate the effect of the acoustic environment on momentary stress and underestimate other confounders.
当研究声学环境与人的健康之间的关系时，存在一个由于数据源自相关而产生的潜在问题。为了解决这个数据源自相关的问题，我们提出了一种第三方评估与人工智能（TPA-AI）模型相结合的方法。TPA-AI 利用声谱图来评估声景的情感质量。首先，我们收集了公众对城市声音的感知数据（即邀请 100 名志愿者对 7051 个 10 秒的音频进行从令人讨厌到令人愉快的两极情感标记）。其次，我们将标记的音频转换为声谱图，并使用深度学习方法训练 TPA-AI 模型，实现了二元分类的 92.88%的预测准确率。第三，采用地理生态瞬间评估（GEMA）记录 180 名参与者在日常生活情境中的瞬间音频，并使用训练有素的 TPA-AI 模型预测这些瞬间音频的情感质量。最后，我们比较了三种方法（即声级计、声音问卷和 TPA-AI 模型）在估计瞬间压力水平与声学环境之间关系时的解释能力。结果表明，TPA-AI 的解释能力优于声级计，而使用声音问卷可能高估了声学环境对瞬间压力的影响，低估了其他混杂因素。
