In this paper, a methodology for the application of neural networks in phase-match calibration of gas–microphone photoacoustics in frequency domain is developed. A two-layer deep neural network is used to determine, in real-time, reliably and accurately, the phase transfer function of the used microphone, applying the photoacoustic response of aluminum as reference material. This transfer function was used to correct the photoacoustic response of laser-sintered polyamide and to compare it with theoretical predictions. The obtained degree of correlation of the corrected and theoretical signal tells us that our method of phase-match calibration in photoacoustics can be generalized to a photoacoustic response coming from a solid sample made of different materials.

中文翻译：

---

深度神经网络在气体-麦克风光声学相位匹配校准中的应用

在本文中，开发了一种将神经网络应用于频域气体-麦克风光声学相位匹配校准的方法。两层深度神经网络用于实时、可靠和准确地确定所用麦克风的相位传递函数，应用铝的光声响应作为参考材料。该传递函数用于校正激光烧结聚酰胺的光声响应，并将其与理论预测进行比较。获得的校正信号和理论信号的相关程度告诉我们，我们在光声学中的相位匹配校准方法可以推广到来自不同材料制成的固体样品的光声响应。