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, a spatiotemporal filter, and other DNN architectures, 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. Code is available on GitHub: https://github.com/nttcslab/deep-sound-field-denoiser.

翻译：本文提出了一种深度声场去噪器，这是一种基于深度神经网络（DNN）的光学测量声场图像去噪方法。利用光学方法进行声场成像因其能够实现传统声学传感器无法达到的高空间分辨率声学现象成像而受到广泛关注。然而，由于光学干涉测量对空气中声波的灵敏度较低，光学测量的声场图像常常受到严重噪声污染。在此，我们提出了一种基于DNN的声场去噪方法。通过时间方向傅里叶变换，将时变声场图像序列分解为谐波复振幅图像。这些复图像被转换为由实部和虚部组成的两通道图像，并通过无非线性激活网络进行去噪。该网络使用通过随机参数数值声学模拟获得的声场数据集进行训练。我们将该方法与常规方法（如图像滤波器、时空滤波器以及其他DNN架构）在数值和实验数据上进行了对比。实验数据通过并行相移干涉测量和全息散斑干涉测量获得。所提出的深度声场去噪器在数值和实验数据上均显著优于常规方法。代码可在GitHub上获取：https://github.com/nttcslab/deep-sound-field-denoiser。