An analysis of the application of neural networks as a reliable, precise, and fast tool in open-cell photoacoustics setups for the recognition of microphone effects in the frequency domain from 10 Hz to 100 × 10⁴ Hz is presented. The network is trained to achieve simultaneous recognition of microphone characteristics, which are the most important parameters leading to the distortion of photoacoustic signals in both amplitude and phase. The training is carried out using a theoretically obtained database of amplitudes and phases as the input and five microphone characteristics as the output, based on transmission measurements obtained using an open photoacoustic cell setup. The results show that the network can precisely and reliably interpolate the output to recognize microphone characteristics including electronic effects in the low and acoustic effects in the high frequency domain. The simulations reveal that the network is not capable of interpolating an input including modulation frequencies. Consequently, in real applications, the network training must be adapted to the experimental frequencies, or vice versa. The total number of frequencies used in the experiment must also be in accordance with the total number of frequencies used in the network training.

中文翻译：

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人工神经网络在固态光声中的应用在频域识别麦克风响应效应

分析神经网络在开孔光声装置中作为可靠，精确且快速的工具的应用，以识别从10 Hz到100×10 ⁴的频域中的麦克风效应 出现Hz。该网络经过培训，可以同时识别麦克风特性，这是导致光声信号在幅度和相位上失真的最重要参数。基于使用开放的光声单元设置获得的传输测量结果，使用理论上获得的幅度和相位数据库作为输入，并使用五个麦克风特性作为输出来进行训练。结果表明，该网络可以精确可靠地对输出进行插值，以识别麦克风特性，包括低频段的电子效果和高频范围的声音效果。仿真表明，网络无法内插包含调制频率的输入。因此，在实际应用中 网络训练必须适应实验频率，反之亦然。实验中使用的频率总数也必须与网络训练中使用的频率总数一致。