TY  - CONF
AU  - Jordović-Pavlović, M.
AU  - Kupusinac, A.
AU  - Đorđević, Katarina Lj.
AU  - Galović, Slobodanka
AU  - Markushev, Dragan
AU  - Nešić, Mioljub V.
AU  - Popović, Marica N.
PY  - 2019
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/11888
AB  - Artificial neural networks as machine learning techniques have proven to be suitable tools for intelligent decision making. This paper presents the application of artificial neural networks for fast and precise characterization of electret microphones by photoacoustic measurements based on optical generation of sound. The transfer function of this type of devices is usually not determined precisely enough by the producers, especially phase transfer function, because such detectors are not widely applied in scientific experiments but are rather used in audio techniques where amplitude transfer function is more important. The distorted photoacoustic experimental signal, influenced by the measurement set-up in a non-linear manner, represents the input of our model, while the outputs are the detector characteristics. The model consists of two neural networks: the first one for the classification of the detector type and the second one for the determination of the detector parameters, related to its electronic and geometric features. Based on this approach and the theoretical model, relying on the acoustics of small volumes, the parameters and transfer characteristics for several microphones are obtained and compared to the characteristics provided by their producers. It has been shown that the suggested method results in much better detector characterization than the one provided in the official specifications. This could be significant not only for scientific applications of microphones but also for their design and applications in audio techniques.
PB  - Belgrade : Vinča Institute of Nuclear Sciences, University of Belgrade
C3  - PHOTONICA2019 : 7th International School and Conference on Photonics & Machine Learning with Photonics Symposium : Book of abstracts
T1  - Computationally intelligent characterization of a photoacoustic detector
SP  - 177
EP  - 177
UR  - https://hdl.handle.net/21.15107/rcub_vinar_11888
ER  - 

@conference{
author = "Jordović-Pavlović, M. and Kupusinac, A. and Đorđević, Katarina Lj. and Galović, Slobodanka and Markushev, Dragan and Nešić, Mioljub V. and Popović, Marica N.",
year = "2019",
abstract = "Artificial neural networks as machine learning techniques have proven to be suitable tools for intelligent decision making. This paper presents the application of artificial neural networks for fast and precise characterization of electret microphones by photoacoustic measurements based on optical generation of sound. The transfer function of this type of devices is usually not determined precisely enough by the producers, especially phase transfer function, because such detectors are not widely applied in scientific experiments but are rather used in audio techniques where amplitude transfer function is more important. The distorted photoacoustic experimental signal, influenced by the measurement set-up in a non-linear manner, represents the input of our model, while the outputs are the detector characteristics. The model consists of two neural networks: the first one for the classification of the detector type and the second one for the determination of the detector parameters, related to its electronic and geometric features. Based on this approach and the theoretical model, relying on the acoustics of small volumes, the parameters and transfer characteristics for several microphones are obtained and compared to the characteristics provided by their producers. It has been shown that the suggested method results in much better detector characterization than the one provided in the official specifications. This could be significant not only for scientific applications of microphones but also for their design and applications in audio techniques.",
publisher = "Belgrade : Vinča Institute of Nuclear Sciences, University of Belgrade",
journal = "PHOTONICA2019 : 7th International School and Conference on Photonics & Machine Learning with Photonics Symposium : Book of abstracts",
title = "Computationally intelligent characterization of a photoacoustic detector",
pages = "177-177",
url = "https://hdl.handle.net/21.15107/rcub_vinar_11888"
}

Jordović-Pavlović, M., Kupusinac, A., Đorđević, K. Lj., Galović, S., Markushev, D., Nešić, M. V.,& Popović, M. N.. (2019). Computationally intelligent characterization of a photoacoustic detector. in PHOTONICA2019 : 7th International School and Conference on Photonics & Machine Learning with Photonics Symposium : Book of abstracts
Belgrade : Vinča Institute of Nuclear Sciences, University of Belgrade., 177-177.
https://hdl.handle.net/21.15107/rcub_vinar_11888

Jordović-Pavlović M, Kupusinac A, Đorđević KL, Galović S, Markushev D, Nešić MV, Popović MN. Computationally intelligent characterization of a photoacoustic detector. in PHOTONICA2019 : 7th International School and Conference on Photonics & Machine Learning with Photonics Symposium : Book of abstracts. 2019;:177-177.
https://hdl.handle.net/21.15107/rcub_vinar_11888 .

Jordović-Pavlović, M., Kupusinac, A., Đorđević, Katarina Lj., Galović, Slobodanka, Markushev, Dragan, Nešić, Mioljub V., Popović, Marica N., "Computationally intelligent characterization of a photoacoustic detector" in PHOTONICA2019 : 7th International School and Conference on Photonics & Machine Learning with Photonics Symposium : Book of abstracts (2019):177-177,
https://hdl.handle.net/21.15107/rcub_vinar_11888 .