This paper proposes a deep sound-field denoiser, a deep neural network (DNN)
based denoising of optically measured sound-field images. Sound-field imaging
using optical methods has gained considerable attention due to its ability to
achieve high-spatial-resolution imaging of acoustic phenomena that conventional
acoustic sensors cannot accomplish. However, the optically measured sound-field
images are often heavily contaminated by noise because of the low sensitivity
of optical interferometric measurements to airborne sound. Here, we propose a
DNN-based sound-field denoising method. Time-varying sound-field image
sequences are decomposed into harmonic complex-amplitude images by using a
time-directional Fourier transform. The complex images are converted into
two-channel images consisting of real and imaginary parts and denoised by a
nonlinear-activation-free network. The network is trained on a sound-field
dataset obtained from numerical acoustic simulations with randomized
parameters. We compared the method with conventional ones, such as image
filters and a spatiotemporal filter, on numerical and experimental data. The
experimental data were measured by parallel phase-shifting interferometry and
holographic speckle interferometry. The proposed deep sound-field denoiser
significantly outperformed the conventional methods on both the numerical and
experimental data.Comment: 13 pages, 8 figures, 2 table