Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network

TL;DR

This paper introduces a CNN-based Near-field Acoustic Holography method that estimates vibrational fields on arbitrary shaped plates with higher resolution and robustness to noise, using simulated data.

Contribution

It presents a novel Super Resolution CNN architecture for NAH on arbitrary shapes, improving resolution and noise robustness over existing methods.

Findings

The CNN accurately predicts vibrational fields on complex shapes.

The method outperforms traditional techniques in resolution and noise robustness.

Validation shows high agreement with ground truth simulations.

Abstract

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.