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Acoustic Resonance Recognition of Coins

: Kraljevski, Ivan; Duckhorn, Frank; Ju, Yong Chul; Tschöpe, Constanze; Richter, Christian; Wolff, Matthias


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Instrumentation and Measurement Society:
IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2020. Conference Proceedings : May 25-29, 2020, Valamar Riviera, Dubrovnik, Croatia
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-4460-3
ISBN: 978-1-7281-4461-0
6 S.
International Instrumentation and Measurement Technology Conference (I2MTC) <2020, Online>
Fraunhofer IKTS ()
coin recognition; deep learning; machine learning; natural frequencies

In this study, we compare different machine learning approaches applied to acoustic resonance recognition of coins. Euro-cents and Euro-coins were classified by the sound emerging when throwing the coins onto a hard surface. The used dataset is a representative example of a small data which was collected in carefully prepared experiments. Due to the small number of coin specimens and the count of the collected observations, it was interesting to see whether deep learning methods can achieve similarly or maybe even better classification performances compared with more traditional methods. The results of the multi-class prediction of coin denominations are presented and compared in terms of balanced accuracy and Matthews Correlation Coefficient metrics. The feature analysis methods combined with the employed classifiers achieved acceptable results, despite the relatively small dataset.