  Accurately representing the sound field with the high spatial resolution is
critical for immersive and interactive sound field reproduction technology. To
minimize experimental effort, data-driven methods have been proposed to
estimate sound fields from a small number of discrete observations. In
particular, kernel-based methods using Gaussian Processes (GPs) with a
covariance function to model spatial correlations have been used for sound
field reconstruction. However, these methods have limitations due to the fixed
kernels having limited expressiveness, requiring manual identification of
optimal kernels for different sound fields. In this work, we propose a new
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 simulated 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.
