Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: EURASIP Journal on Audio, Speech, and Music Processing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.