Authors: Jordović-Pavlović, Miroslava
Stanković, Miomir 
Popović, M. N.
Ćojbašić, Žarko
Galović, S. P.
Markushev, Dragan
Title: The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain
Journal: Journal of Computational Electronics
Volume: 19
Issue: 3
First page: 1268
Last page: 1280
Issue Date: 1-Sep-2020
Rank: M23
ISSN: 1569-8025
DOI: 10.1007/s10825-020-01507-4
Abstract: 
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.
Keywords: Intelligent instruments | Microphone response | Modulation frequency | Neural networks | Photoacoustics
Publisher: Springer Link

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