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 fr...equencies. 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.
TY - JOUR
AU - Jordović-Pavlović, Miroslava I.
AU - Stanković, Milena M.
AU - Popović, Marica N.
AU - Ćojbašić, Žarko М.
AU - Galović, Slobodanka
AU - Markushev, Dragan D.
PY - 2020
UR - https://vinar.vin.bg.ac.rs/handle/123456789/9005
AB - 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.
T2 - Journal of Computational Electronics
T1 - The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain
VL - 19
IS - 3
SP - 1268
EP - 1280
DO - 10.1007/s10825-020-01507-4
ER -
@article{
author = "Jordović-Pavlović, Miroslava I. and Stanković, Milena M. and Popović, Marica N. and Ćojbašić, Žarko М. and Galović, Slobodanka and Markushev, Dragan D.",
year = "2020",
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.",
journal = "Journal of Computational Electronics",
title = "The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain",
volume = "19",
number = "3",
pages = "1268-1280",
doi = "10.1007/s10825-020-01507-4"
}
Jordović-Pavlović, M. I., Stanković, M. M., Popović, M. N., Ćojbašić, Ž. М., Galović, S.,& Markushev, D. D.. (2020). The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain. in Journal of Computational Electronics, 19(3), 1268-1280.
https://doi.org/10.1007/s10825-020-01507-4
Jordović-Pavlović MI, Stanković MM, Popović MN, Ćojbašić ŽМ, Galović S, Markushev DD. The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain. in Journal of Computational Electronics. 2020;19(3):1268-1280.
doi:10.1007/s10825-020-01507-4 .
Jordović-Pavlović, Miroslava I., Stanković, Milena M., Popović, Marica N., Ćojbašić, Žarko М., Galović, Slobodanka, Markushev, Dragan D., "The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain" in Journal of Computational Electronics, 19, no. 3 (2020):1268-1280,
https://doi.org/10.1007/s10825-020-01507-4 . .
, 