Search Results for author:
Found 7 papers, 3 papers with code
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.
A convolutional plane wave model for sound field reconstruction
A suitable model can be difficult to determine when the spatial domain of interest is large compared to the wavelength or when spherical and planar wavefronts are present or the sound field is complex, as in the near-field.
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).
Semi-supervised source localization in reverberant environments with deep generative modeling
As far as we are aware, our paper presents the first approach to modeling the physics of acoustic propagation using deep generative modeling.
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.
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).
In situ sound absorption estimation with the discrete complex image source method
This article proposes a method for estimating the sound absorption coefficient of a material sample by mapping the sound pressure, measured by a microphone array, to a distribution of monopoles along a line in the complex plane.
