Development of an Annoyance Prediction Model for simultaneous Exposure to Noise and Vibrations

开发同时暴露于噪声和振动的烦恼预测模型

• 批准号: 376265058
• 负责人: Professor Dr.-Ing. Ercan Altinsoy
• 金额: --
• 依托单位: Professur für Akustik und Haptik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/376265058?language=en
• 关键词: Development; Annoyance; Prediction; Model; simultaneous

Noise and vibrations effect humans in many daily situations. One typical example is driving a motor vehicle. The stimuli emerging in such situations are usually perceived as annoying. This can be described by the perception parameter annoyance. For rating and comparing the annoyance of different exposure situations, time-consuming and technically demanding perception experiments have to be conducted.Whenever acoustic and vibratory stimuli occur simultaneously, they are jointly processed as one common overall perception event. In such a way the annoyance of multimodal exposure situations is depend on the properties of both of the stimulations. To anticipate the human perception directly from measured signals, a model for the prediction of the annoyance based on the signal properties of multimodal scenes shall be created.For purely acoustic stimuli, the perceived annoyance can already be modelled from the signals parameters. For multimodal stimuli, available studies have already revealed fundamental relationships between the acoustic and vibratory signal properties and the resulting perception values.On this basis, the first step of the planned research project is creating a multimodal exposure database. Therefore sound pressure und seat vibration signals in different kinds of vehicles and at different driving and environmental situations shall be acquired. Specific manipulations of certain signal properties create further realistic scene variations. Thereby the entirety of the created multimodal scenes represents a broad range of realistic exposure situations in motor vehicles.Subsequently the annoyance ratings for these scenes are being acquired by perception experiments at the multimodal measurement laboratory.Statistical analysis of the results forms the basis for the creation of an annoyance rating model for the multimodal scenes. Taking into consideration the temporal and spectral characteristics of both the sound and whole-body vibration signals, both physical and psychophysical parameters will be used as input values for this model.The developed forecast model shall provide the ability of estimating the annoyance of a multimodal scene, such as the strain of a vehicle operator in different situations, without the necessity of conducting perception experiments. Furthermore it can be clarified, under which circumstances either the acoustic or vibratory exposures are dominating, what makes a solely unimodal consideration of the exposure situation sufficient for the evaluation of the perceived annoyance.

噪音和振动在许多日常情况下影响人类。一个典型的例子是驾驶汽车。在这种情况下出现的刺激通常被认为是恼人的。这可以通过感知参数烦恼来描述。为了评估和比较不同暴露情况下的烦恼，必须进行耗时且技术要求高的感知实验。当声音和振动刺激同时发生时，它们被联合处理为一个共同的整体感知事件。以这种方式，多模态暴露情况的烦恼取决于两种刺激的性质。为了直接从测量信号中预测人的感知，需要建立一个基于多模态场景信号特性的烦恼预测模型。对于纯声学刺激，感知烦恼已经可以从信号参数中建模。对于多模态刺激，现有的研究已经揭示了声学和振动信号特性与所产生的感知值之间的基本关系。在此基础上，计划研究项目的第一步是创建多模态暴露数据库。因此，需要采集不同车型、不同行驶工况和环境条件下的声压和座椅振动信号。对某些信号特性的特定操纵产生了进一步的真实场景变化。因此，所创建的多模态场景的整体代表了机动车辆中广泛的真实暴露情况。随后，在多模态测量实验室通过感知实验获得这些场景的烦恼等级。对结果的统计分析构成了创建多模态场景烦恼等级模型的基础。考虑到声音和全身振动信号的时间和频谱特性，物理和心理物理参数将被用作该模型的输入值。开发的预测模型应提供估计多模态场景的烦恼的能力，例如车辆操作员在不同情况下的应变，而无需进行感知实验。此外，可以澄清的是，在何种情况下，声学或振动暴露占主导地位，是什么使得暴露情况的单独单峰考虑足以评估感知的烦恼。