Generative adversarial networks with physical sound field priors

• URL: http://arxiv.org/abs/2308.00426v1
• Date: Tue, 1 Aug 2023 10:11:23 GMT
• Title: Generative adversarial networks with physical sound field priors
• Authors: Xenofon Karakonstantis and Efren Fernandez-Grande
• Abstract summary: This paper presents a deep learning-based approach for learns-temporal reconstruction of sound fields using Generative Adversa Networks (GANs) The proposed method uses a plane wave basis and the underlying statistical distributions of pressure in rooms to reconstruct sound fields from a limited number of measurements. The results suggest that this approach provides a promising approach to sound field reconstruction using generative models that allow for a physically informed acoustics prior to problems.
• Score: 6.256923690998173
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a deep learning-based approach for the spatio-temporal reconstruction of sound fields using Generative Adversarial Networks (GANs). The method utilises a plane wave basis and learns the underlying statistical distributions of pressure in rooms to accurately reconstruct sound fields from a limited number of measurements. The performance of the method is evaluated using two established datasets and compared to state-of-the-art methods. The results show that the model is able to achieve an improved reconstruction performance in terms of accuracy and energy retention, particularly in the high-frequency range and when extrapolating beyond the measurement region. Furthermore, the proposed method can handle a varying number of measurement positions and configurations without sacrificing performance. The results suggest that this approach provides a promising approach to sound field reconstruction using generative models that allow for a physically informed prior to acoustics problems.

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