Sound field reconstruction using neural processes with dynamic kernels

Accurately representing the sound field with high spatial resolution is crucial for immersive and interactive sound field reproduction technology. In recent studies, there has been a notable emphasis on efficiently estimating sound fields from a limited number of discrete observations. In particular, kernel-based methods using Gaussian processes (GPs) with a covariance function to model spatial correlations have been proposed. However, the current methods rely on pre-defined kernels for modeling, requiring the manual identification of optimal kernels and their parameters for different sound fields. In this work, we propose a novel approach that parameterizes GPs using a deep neural network based on neural processes (NPs) to reconstruct the magnitude of the sound field. This method has the advantage of dynamically learning kernels from data using an attention mechanism, allowing for greater flexibility and adaptability to the acoustic properties of the sound field. Numerical experiments demonstrate that our proposed approach outperforms current methods in reconstructing accuracy, providing a promising alternative for sound field reconstruction.