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Found 4 papers, 2 papers with code
Efficient Sound Field Reconstruction with Conditional Invertible Neural Networks
In this study, we introduce a method for estimating sound fields in reverberant environments using a conditional invertible neural network (CINN).
Physics-Informed Neural Network for Volumetric Sound field Reconstruction of Speech Signals
Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based approaches, particularly in sound field reconstruction.
Room impulse response reconstruction with physics-informed deep learning
A method is presented for estimating and reconstructing the sound field within a room using physics-informed neural networks.
Generative adversarial networks with physical sound field priors
This paper presents a deep learning-based approach for the spatio-temporal reconstruction of sound fields using Generative Adversarial Networks (GANs).
