Generalizing synthetic data-trained acoustic predictive models to real-world measurements
IntroductionAcoustic Resonance Spectroscopy (ARS) is highly sensitive to structural properties such as material, geometry, and environmental conditions; as a consequence, it can noninvasively measure internal properties that are unobservable by most other methods. Because of its sensing capabilities...
محفوظ في:
| المؤلفون الرئيسيون: | , , , , , , |
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| التنسيق: | Artigo |
| اللغة: | Inglês |
| منشور في: |
Frontiers Media S.A.
2026-06-01
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| سلاسل: | Frontiers in Acoustics |
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://www.frontiersin.org/articles/10.3389/facou.2026.1867954/full |
| الوسوم: |
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