Radar based deep learning technology for loudspeaker faults detection and classification

Radar Based Loudspeaker Measurements

“…Finally, a matched filter approach was proposed to retrieve and characterise the mechanical response of the transducer. Accordingly, the power spectrum density, linear frequency response, and higher harmonics, when used as features for an automatic classifier, proved to be effective detectors of loudspeakers' manufacturing problems [3].…”

“…20, the harmonic products affect the behaviour of the speaker mainly at low frequency, where the device is more susceptible to the non-linear effects, in agreement with loudspeaker model theory. Based on the methodology to measure mechanical frequency response of loudspeakers introduced above, a first attempt of loudspeaker faults detection and classification was shown in [3] thanks to a joint radar micro-Doppler and deep learning technology for End-Of-Line (EOL) test.…”

“…In this paper we want to discuss possible applications of the technology presented in [1], [2], [3]. In particular the use of radar micro-Doppler paired with sine-sweep measurements will be shown to be a powerful method to retrieve the mechanical response of an electro-dynamic loudspeaker, as well as a versatile End Of Line (EOL) classifier algorithm.…”