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Identifying sources of variability in impedance tube sound absorption measurements with multivariate explainable machine learning
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DOI：10.1016/j.jsv.2026.120003.png)
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Abstract
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• Data-driven classification applied to impedance tube SAC measurements. • Variability sources in SAC measurements systematically investigated. • Edge constraints significantly affect absorption of porous samples. • Sample rotation has negligible impact across thickness and materials. • SHAP identifies frequency bands sensitive to experimental factors.
Keywords:
Impedance tube
Measurement source of variability
Sound absorption coefficient
Supervised machine learning
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