  Near-field Acoustic Holography (NAH) is a well-known problem aimed at
estimating the vibrational velocity field of a structure by means of acoustic
measurements. In this paper, we propose a NAH technique based on Convolutional
Neural Network (CNN). The devised CNN predicts the vibrational field on the
surface of arbitrary shaped plates (violin plates) with orthotropic material
properties from a limited number of measurements. In particular, the
architecture, named super resolution CNN (SRCNN), is able to estimate the
vibrational field with a higher spatial resolution compared to the input
pressure. The pressure and velocity datasets have been generated through Finite
Element Method simulations. We validate the proposed method by comparing the
estimates with the synthesized ground truth and with a state-of-the-art
technique. Moreover, we evaluate the robustness of the devised network against
noisy input data.
