Computationally intelligent estimation of properties for polymer microphone diaphragms by photoacoustic measurement

Abstract

This paper presents the application of artificial neural networks for fast and precise characterization of electret microphones with polymer transducer (diaphragm) by photoacoustic measurements. The model consists of two neural networks: the first one for the classification of the microphone type and the second one for the determination of the detector parameters, related to its electronic and geometric features as well as to piezoelectric transducer properties. Obtained prediction has been used for estimation of polymer diaphragms properties by employment of Helmholtz model for sound propagation in small volumes.