Radar based deep learning technology for loudspeaker faults detection and classification

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: IEEE Radar Conference 2020, 2020-09-21 - 2020-09-25, Florence. (In Press)

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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.