Radar based deep learning technology for loudspeaker faults detection and classification

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Izzo, A. and Clemente, C. and Ausiello, L. and Soraghan, J.J.; (2020) Radar based deep learning technology for loudspeaker faults detection and classification. In: 2020 IEEE Radar Conference (RadarConf20). IEEE, ITA. ISBN 9781728189420 (https://doi.org/10.1109/RadarConf2043947.2020.9266...)

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Abstract

Recently, radar based micro-Doppler signature analysis has been successfully applied in various sectors including both defence and civilian applications. A joint radar micro-Doppler and deep learning technology for End-Of-Line (EOL)test of loudspeakers is proposed in this paper. This approach offers the potential benefits of characterizing the mechanical motion of a loudspeaker in a noisy environment as a production line, in order to automatically identify and classify defects. Starting from real radar signal, the proposed Bidirectional Long Short-Term Memory (BiLSTM) classifier has been tested on training, validation and test dataset. The results show that the proposed approach produces a probability of correct classification abovethe98%, outperforming the traditional k-NN classifier.

ORCID iDs

Izzo, A. ORCID logoORCID: https://orcid.org/0000-0001-6009-8757, Clemente, C. ORCID logoORCID: https://orcid.org/0000-0002-6665-693X, Ausiello, L. and Soraghan, J.J. ORCID logoORCID: https://orcid.org/0000-0003-4418-7391;
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