Bibliographic Details
| Title: |
A Zero-Shot Physics-Informed Dictionary Learning Approach for Sound Field Reconstruction |
| Authors: |
Damiano, Stefano, Miotello, Federico, Pezzoli, Mirco, Bernardini, Alberto, Antonacci, Fabio, Sarti, Augusto, van Waterschoot, Toon |
| Publication Year: |
2024 |
| Subject Terms: |
Electrical Engineering and Systems Science - Audio and Speech Processing, Electrical Engineering and Systems Science - Signal Processing |
| More Details: |
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 data availability or the explicit modeling of physical properties. To bridge these gaps, this study introduces a zero-shot, physics-informed dictionary learning approach to perform sound field reconstruction. Our method relies only on a few sparse measurements to learn a dictionary, without the need for additional training data. Moreover, by enforcing the Helmholtz equation during the optimization process, the proposed approach ensures that the reconstructed sound field is represented as a linear combination of a few physically meaningful atoms. Evaluations on real-world data show that our approach achieves comparable performance to state-of-the-art dictionary learning techniques, with the advantage of requiring only a few observations of the sound field and no training on a dataset.
Comment: Accepted for publication at ICASSP 2025 |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2412.18348 |
| Accession Number: |
edsarx.2412.18348 |
| Database: |
arXiv |
