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Physics-Informed Transfer Learning for Data-Driven Sound Source Reconstruction in Near-Field Acoustic Holography
Xinmeng Luan, Mirco Pezzoli, Fabio Antonacci +1
We propose a transfer learning framework for sound source reconstruction in Near-field Acoustic Holography (NAH), which adapts a well-trained data-driven model from one type of sou…
Physics-Informed Neural Network-Driven Sparse Field Discretization Method for Near-Field Acoustic Holography
Xinmeng Luan, Mirco Pezzoli, Fabio Antonacci +1
We propose the Physics-Informed Neural Network-driven Sparse Field Discretization method (PINN-SFD), a novel self-supervised, physics-informed deep learning approach for addressing…
A Zero-Shot Physics-Informed Dictionary Learning Approach for Sound Field Reconstruction
Stefano Damiano, Federico Miotello, Mirco Pezzoli +4
Sound field reconstruction aims to estimate pressure fields in areas lacking direct measurements. Existing techniques often rely on strong assumptions or face challenges related to…
A Physics-Informed Neural Network-Based Approach for the Spatial Upsampling of Spherical Microphone Arrays
Federico Miotello, Ferdinando Terminiello, Mirco Pezzoli +3
Spherical microphone arrays are convenient tools for capturing the spatial characteristics of a sound field. However, achieving superior spatial resolution requires arrays with num…
Interpreting End-to-End Deep Learning Models for Speech Source Localization Using Layer-wise Relevance Propagation
Luca Comanducci, Fabio Antonacci, Augusto Sarti
Deep learning models are widely applied in the signal processing community, yet their inner working procedure is often treated as a black box. In this paper, we investigate the use…
Physics-Informed Neural Network for Volumetric Sound field Reconstruction of Speech Signals
Marco Olivieri, Xenofon Karakonstantis, Mirco Pezzoli +3
Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based app…