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

噪声通过影响建筑物居住者的舒适度和性能对空间质量有显著影响。在噪声控制领域中，仅试图降低物理噪声水平的传统方法具有某些固有的局限性。这些方法往往忽视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，则量化无人条件下的声学质量的努力可能无法满足用户的声学要求。因此，结合乘员舒适性的优化的噪声控制产品设计是一项尚未得到充分确立的重要知识。 因此，拟议的研究计划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）在占用的教室中开发自动活动噪声水平检测方法，（B）优化用于音调HVAC噪声的微穿孔板嵌入式消音器，以及（c）优化用于占用的开放式办公室的声音掩蔽系统。 为了实现研究建议中概述的目标，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，将收集占用空间中的声学数据，以获得每种空间类型的占用模式。建筑空间中准确的背景噪声谱将可用于预测声学活动。将使用高斯混合模型等无监督机器学习算法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系统的管道消音器将通过识别关键部件来优化。最后，将在虚拟现实实验室中验证基于降噪技术优化的乘员舒适性改善。 本研究的结果将提供一个优化的方法，设计噪声控制技术在占用的室内空间。本研究的成功完成，可望提高管道消声器和声掩蔽系统设计的成本效益。研究结果也可作为可持续建筑的循证设计方法。这些努力的最终结果将是实现建筑空间的最佳声学质量，从而提高居住者的舒适度和建筑环境的性能。

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.