Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network
This work addresses a domain-specific problem in acoustics for structures like violin plates, offering an incremental improvement by applying a CNN to an existing NAH framework.
The paper tackles the problem of estimating vibrational velocity fields on arbitrary-shaped plates using Near-field Acoustic Holography (NAH) by proposing a Convolutional Neural Network (CNN) technique, achieving higher spatial resolution compared to input pressure and validating it against ground truth and state-of-the-art methods with robustness to noise.
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.