Radar based deep learning technology for loudspeaker faults detection and classification

A. Izzo, C. Clemente, L. Ausiello, J.J. Soraghan

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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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.
Original languageEnglish
Title of host publication2020 IEEE Radar Conference (RadarConf20)
Place of PublicationPiscataway, NJ.
PublisherIEEE
Number of pages6
ISBN (Electronic)9781728189420
ISBN (Print)9781728189437
DOIs
Publication statusPublished - 4 Dec 2020
EventIEEE Radar Conference 2020 - Florence, Florence, Italy
Duration: 21 Sep 202025 Sep 2020

Conference

ConferenceIEEE Radar Conference 2020
CountryItaly
CityFlorence
Period21/09/2025/09/20

Keywords

  • civilian radar application
  • micro-doppler analysis
  • loudspeaker analysis
  • deep learning

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