A Physics-Informed Neural Network-Based Approach for the Spatial Upsampling of Spherical Microphone Arrays
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910543769501696 |
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| author | Miotello, Federico Terminiello, Ferdinando Pezzoli, Mirco Bernardini, Alberto Antonacci, Fabio Sarti, Augusto |
| author_facet | Miotello, Federico Terminiello, Ferdinando Pezzoli, Mirco Bernardini, Alberto Antonacci, Fabio Sarti, Augusto |
| contents | Spherical microphone arrays are convenient tools for capturing the spatial characteristics of a sound field. However, achieving superior spatial resolution requires arrays with numerous capsules, consequently leading to expensive devices. To address this issue, we present a method for spatially upsampling spherical microphone arrays with a limited number of capsules. Our approach exploits a physics-informed neural network with Rowdy activation functions, leveraging physical constraints to provide high-order microphone array signals, starting from low-order devices. Results show that, within its domain of application, our approach outperforms a state of the art method based on signal processing for spherical microphone arrays upsampling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18732 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Physics-Informed Neural Network-Based Approach for the Spatial Upsampling of Spherical Microphone Arrays Miotello, Federico Terminiello, Ferdinando Pezzoli, Mirco Bernardini, Alberto Antonacci, Fabio Sarti, Augusto Audio and Speech Processing Machine Learning Sound Signal Processing Spherical microphone arrays are convenient tools for capturing the spatial characteristics of a sound field. However, achieving superior spatial resolution requires arrays with numerous capsules, consequently leading to expensive devices. To address this issue, we present a method for spatially upsampling spherical microphone arrays with a limited number of capsules. Our approach exploits a physics-informed neural network with Rowdy activation functions, leveraging physical constraints to provide high-order microphone array signals, starting from low-order devices. Results show that, within its domain of application, our approach outperforms a state of the art method based on signal processing for spherical microphone arrays upsampling. |
| title | A Physics-Informed Neural Network-Based Approach for the Spatial Upsampling of Spherical Microphone Arrays |
| topic | Audio and Speech Processing Machine Learning Sound Signal Processing |
| url | https://arxiv.org/abs/2407.18732 |