Deep Neural Network (DNN) models offer an attractive alternative to existing acoustic source imaging techniques, such as acoustic beamforming, due to their ever-growing potential with increasing computational power. Source resolution of acoustic beamforming methods is limited at lower frequencies and their source maps may possess sidelobes at higher frequencies. However, acoustic beamforming methods are typically robust over a wide range of simulation and experimental conditions, such as (i) the number of sources present, (ii) source frequency and (ii) extraneous noise sources. The performance of DNN models, when these conditions are varied from their specific design criteria, is yet to be investigated and much work is needed in this area before DNN models can be utilized in experiments, such as wind tunnel tests. Furthermore, few studies have been conducted on experimental validation of DNN models, predominately due to the difficulty of large sets of experimentally obtained data needed for DNN model training and the sensitivity of DNN model performance when any of the aforementioned experimental conditions are varied. In this paper, a series of studies on the robustness of DNN models based on numerical data and experimental data are presented. Numerical DNN (NDNN) models are trained using in-phase and random-phase pressure data generated from six sources over design frequencies from 500 Hz to 20,000 Hz. The robustness of the NDNN models is tested via (1) inclusion of extraneous Gaussian white noise, (2) inclusion of extraneous tonal noise near the design frequency, (3) using source frequencies that slightly differ from the design frequencies and (4) using a number of sources that differs from the design source number. DNN model performance metrics are introduced that present a promising framework for future DNN model studies and bridging the gap between NDNN and experimentally trained DNN models. A preliminary experimental validation was conducted using a single speaker that was systematically placed over a speaker grid to generate training data via acoustic superposition, from which an experimentally trained DNN (EDNN) model was produced. The EDNN model yields exceptional noise source localization capability of the DNN model, revealing a promising start for a more sophisticated EDNN model.

中文翻译：

---

用于声源成像的深度神经网络的鲁棒性分析和实验验证


深度神经网络 (DNN) 模型为现有声源成像技术（例如声波束形成）提供了一种有吸引力的替代方案，因为它们的潜力随着计算能力的增加而不断增长。声波束形成方法的源分辨率在较低频率下受到限制，并且它们的源图在较高频率下可能具有旁瓣。然而，声波束形成方法通常在各种模拟和实验条件下都很稳健，例如（i）存在的源数量，（ii）源频率和（ii）外部噪声源。当这些条件与其具体设计标准不同时，DNN 模型的性能还有待研究，并且在将 DNN 模型用于风洞测试等实验之前，还需要在该领域开展大量工作。此外，对 DNN 模型的实验验证进行的研究很少，主要是由于 DNN 模型训练所需的大量实验获得数据的难度以及当上述任何实验条件发生变化时 DNN 模型性能的敏感性。本文提出了一系列基于数值数据和实验数据的 DNN 模型鲁棒性研究。数值 DNN (NDNN) 模型使用从 6 个源生成的同相和随机相压力数据进行训练，设计频率范围为 500 Hz 至 20,000 Hz。 NDNN 模型的稳健性通过以下方式进行测试：(1) 包含无关的高斯白噪声，(2) 包含设计频率附近的无关音调噪声，(3) 使用与设计频率略有不同的源频率，以及 (4) 使用与设计源编号不同的多个源。 引入了 DNN 模型性能指标，为未来的 DNN 模型研究提供了一个有前途的框架，并弥合了 NDNN 和经过实验训练的 DNN 模型之间的差距。使用系统放置在扬声器网格上的单个扬声器进行初步实验验证，通过声学叠加生成训练数据，并从中生成经过实验训练的 DNN (EDNN) 模型。 EDNN 模型产生了 DNN 模型卓越的噪声源定位能力，为更复杂的 EDNN 模型提供了一个良好的开端。