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A boundary element model for acoustic responses in the ear canal and its statistical validation and updating

机译:耳道声学响应的边界元模型及其统计验证和更新

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摘要

A boundary element method (BEM) model for acoustic responses in individual ear canals was developed, and its validity was assessed and updated using a statistical approach. The BEM model was developed using high resolution computed tomography (CT) scanning from a healthy male adult. The BEM model, which was discretized adaptively for a frequency band of up to 22 kHz, included the head, pinna, ear canals, and tympanic membranes. The variability of the BEM model due to the uncertain acoustic boundary conditions and measurement locations was predicted using the eigenvector dimension reduction (EDR) method. Then, the likelihood function estimation approach was introduced in order to measure the agreement between the acoustic responses of the simulation model and experimental results. In order to enhance the simulation model performance, the acoustic boundary conditions of the BEM model were updated using a statistical calibration approach that maximizes the likelihood function value between the calculated probability density functions (PDFs) of the simulation model and the measurement data. The results of the validation and calibration procedures applied to the BEM model demonstrated that the proposed method provides a very effective method of verifying the model validity and enhancing the performance of the simulation model.
机译:建立了用于单个耳道声学响应的边界元方法(BEM)模型,并使用统计方法评估和更新了其有效性。 BEM模型是使用来自健康男性成年人的高分辨率计算机断层扫描(CT)扫描开发的。 BEM模型可针对高达22 kHz的频带进行自适应离散,包括头,耳廓,耳道和鼓膜。使用特征向量降维(EDR)方法预测了由于不确定的声边界条件和测量位置而引起的BEM模型的可变性。然后,引入似然函数估计方法,以测量仿真模型的声学响应与实验结果之间的一致性。为了增强仿真模型的性能,使用统计校准方法更新了BEM模型的声学边界条件,该方法将计算出的仿真模型的概率密度函数(PDF)与测量数据之间的似然函数值最大化。应用于BEM模型的验证和校准程序的结果表明,该方法提供了一种非常有效的方法来验证模型的有效性并增强仿真模型的性能。

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