Human-centered design optimization for noise control technologies in built environments

噪声通过影响建筑居住者的舒适度和工作性能，对空间质量产生重大影响。在噪声控制领域，传统的方法仅试图降低物理噪声水平，具有某些固有的局限性。这些方法往往忽略了居住者的特征和行为。如果不仔细考虑空间中的居住者模式，在无人居住的条件下量化声学质量的努力可能无法满足用户的声学要求。因此，结合乘员舒适度的优化噪声控制产品设计是一项尚未完善的重要知识。因此，拟议研究项目的长期目标是通过提供以人为本的声学设计优化方法来改善建筑空间的声学舒适性。短期目标是：(a)在被占用的教室中开发一种自动活动噪声水平检测方法，(b)优化微孔嵌板消声器以抑制调性暖通空调噪声，以及(c)优化被占用的开放式办公室的掩声系统。为了达到研究计划中概述的目标，将进行一系列声学测量、数据分析、数值模拟和主观测试。首先，收集被占用空间的声学数据，获得每种空间类型的占用模式。建筑物空间的准确背景噪声谱将用于预测声学活动。无监督机器学习算法（如高斯混合模型）将用于对背景噪声水平进行分类。教室的语音清晰度，开放式办公室的声音掩蔽系统，以及HVAC系统的管道消声器将通过确定关键组件来优化。最后，基于优化降噪技术的乘员舒适度改善将在虚拟现实实验室中进行验证。本研究结果将为室内空间噪声控制技术的设计提供一种优化方法。该研究的成功完成有望提高管道消声器和掩声系统设计的成本效益。研究结果也可以作为可持续建筑的循证设计方法。这些努力的最终成果将是在建筑空间中实现最佳的声学质量，从而提高居住者在建筑环境中的舒适度和性能。

Noise has a significant effect on the quality of space by influencing building occupant comfort and performance. Traditional approaches in the noise control area that attempt only to reduce physical noise levels have certain inherent limitations. These approaches tend to overlook occupant characteristics and behaviours. Without careful consideration of occupant patterns in spaces, efforts to quantify acoustic quality in unoccupied conditions can fail to meet the acoustic requirements for users. An optimized noise control product design incorporating occupant comfort is thus a significant piece of knowledge that has not yet been well-established. The long-term objective of the proposed research program is, therefore, to improve acoustic comfort in building spaces by providing a human-centered acoustic design optimization methodology. The short-term objectives are: (a) to develop an automatic activity noise level detection method in occupied classrooms, (b) to optimize a microperforated panel-embedded silencer for tonal HVAC noise, and (c) to optimize a sound masking system for occupied open-plan offices. To achieve the objectives outlined in the research proposal, a series of acoustic measurements, data analyses, numerical simulations, and subjective tests will be conducted. First, acoustic data in occupied spaces will be collected to obtain occupancy patterns for each space type. Accurate background noise spectra in building spaces will be made available for predicting acoustic activities. Unsupervised machine learning algorithms such as a Gaussian mixture model will be used to classify the background noise levels. The speech intelligibility for classrooms, sound masking system for open-plan offices, and a duct silencer for HVAC systems will be optimized by identifying crucial components. Finally, improved occupant comfort based on optimization of the noise reduction technologies will be validated in a virtual reality laboratory. The results of this research will provide an optimization methodology for designing noise-control technologies in occupied indoor spaces. The successful completion of the proposed research can be expected to improve the cost-effectiveness of the duct silencer and sound masking system design. The outcomes also can be used as an evidence-based design approach for sustainable buildings. The culmination of these efforts will be the achievement of optimal acoustic quality in building spaces, thereby improving occupant comfort and performance in the built environment.