Imperial College London
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Professor Norbert Hoffmann

Faculty of Engineering, Department of Mechanical Engineering

Visiting Professor
 
 
 
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Contact

 

n.hoffmann

 
 
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Location

 

557City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Stender:2021:10.3397/IN2021-2342,
author = {Stender, M and Wedler, M and Hoffmann, N and Adams, C},
doi = {10.3397/IN2021-2342},
title = {Explainable machine learning: A case study on impedance tube measurements},
url = {http://dx.doi.org/10.3397/IN2021-2342},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Machine learning techniques allow for finding hidden patterns and signatures in data. Currently, these methods are gaining increased interest in engineering in general and in vibroacoustics in particular. Although ML methods are successfully applied, it is hardly understood how these black-box-type methods make their decisions. Explainable machine learning aims at overcoming this issue by deepen the understanding on the decision making process through perturbation-based model diagnosis. This paper introduces machine learning methods and reviews recent techniques for explainability and interpretability. These methods are exemplified on sound absorption coefficient spectra of one sound absorbing foam material measured in an impedance tube. Variances of the absorption coefficients measurements as a function of the specimen thickness and the operator are modeled by univariate and multivariate machine learning models. In order to identify the driving patterns, i.e., how and in which frequency regime the measurements are affected by the setup specifications, Shapley additive explanations are derived for the ML models. It is demonstrated how explaining machine learning models can be used to discover and express complicated relations in experimental data, thereby paving the way to novel knowledge discovery strategies in evidence-based modeling.
AU  - Stender,M
AU  - Wedler,M
AU  - Hoffmann,N
AU  - Adams,C
DO  - 10.3397/IN2021-2342
PY  - 2021///
TI  - Explainable machine learning: A case study on impedance tube measurements
UR  - http://dx.doi.org/10.3397/IN2021-2342
ER  -
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