Noise reduction combining microphones and piezoelectric devices

Mitsuharu Matsumoto*, Shuji Hashimoto

*Corresponding author for this work

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

    5 Citations (Scopus)

    Abstract

    This paper describes noise reduction combining microphones and piezoelectric devices. The conventional microphone array has widely been utilized to realize a noise reduction and Direction of Arrival (DOA) estimation system. However, when the microphones are mounted to robot systems, the microphones are set extremely close to the robot. Then, the problem of internal noise from robots such as motors and gears arises. It is difficult to reduce the internal noise utilizing the conventional microphone array because the noise source is extremely close to the microphones. As the internal noise is not always stationary or sparse, we also can not employ useful blind source separation (BSS) approaches such as Independent Component Analysis (ICA) and sparseness approach. The aim of our research is to reduce the internal noise by utilizing the piezoelectric devices attached on the internal noise source. In this paper, we formulate the problem combining microphones and the piezoelectric devices and describe the proposed algorithm for noise reduction. Experimental results show that our proposed method can reduce the internal noise.

    Original languageEnglish
    Title of host publication2008 3rd International Symposium on Communications, Control, and Signal Processing, ISCCSP 2008
    Pages368-371
    Number of pages4
    DOIs
    Publication statusPublished - 2008
    Event2008 3rd International Symposium on Communications, Control, and Signal Processing, ISCCSP2008 - St. Julians
    Duration: 2008 Mar 122008 Mar 14

    Other

    Other2008 3rd International Symposium on Communications, Control, and Signal Processing, ISCCSP2008
    CitySt. Julians
    Period08/3/1208/3/14

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Signal Processing
    • Control and Systems Engineering

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