Near-field acoustic holography analysis with convolutional neural networks

Near-field Acoustic Holography (NAH) enables the contactless analysis of the vibrational field on plates and shells from the acoustic data captured in proximity of the vibrating object. In this paper, we propose a data-driven approach to NAH by using a Convolutional Neural Network (CNN) that predicts the vibrational field on the object from the acoustic pressure field captured by a microphone array deployed in its proximity. We have conducted an extensive simulation campaign on rectangular plates of different dimensions, boundary conditions and mechanical properties. This dataset has been generated using Finite Element Method simulation for predicting both vibrational and acoustic pressure fields. The performance of the proposed data-driven NAH method is assessed by comparing the estimated vibrational field with the ground truth. Moreover, we offer an analysis of the robustness of the estimate against noisy input data.