Fast and efficient least-mean-squares algorithm for active noise control system identification

Rahimie Mustafa, Mohd Alauddin Mohd Ali

    Research output: Contribution to journalArticle

    1 Citation (Scopus)

    Abstract

    A fast and efficient least-mean-squares algorithm for active noise control system identification is presented. The algorithm is incorporated into field programmable gate arrays (FPGA) to ensure real-time and fast convergence. Adaptive system identification for ANC headset application is carried out to validate the algorithm effectiveness. A 64-tap FIR-based LMS adaptive filter is designed on the basis of the multiplier-adder structure and is synthesized onto the Cyclone II FPGA of the Altera DE2 development board. The headset used is the HD280 model from Sennheiser and has been modified by inserting a small microphone through the earmuff. In this experiment, a mannequin wearing the headset is used to replicate a human being. The result shows that in a time-varying situation, repeated weight updates in every audio sample will guarantee error convergence within the period of audio sampling itself.

    Original languageEnglish
    Pages (from-to)111-112
    Number of pages2
    JournalAcoustical Science and Technology
    Volume33
    Issue number2
    DOIs
    Publication statusPublished - 2012

    Fingerprint

    earphones
    system identification
    field-programmable gate arrays
    adding circuits
    adaptive filters
    cyclones
    taps
    multipliers
    microphones
    sampling

    Keywords

    • Active noise control
    • Fast convergence
    • Least-mean-squares
    • System identification

    ASJC Scopus subject areas

    • Acoustics and Ultrasonics

    Cite this

    Fast and efficient least-mean-squares algorithm for active noise control system identification. / Mustafa, Rahimie; Ali, Mohd Alauddin Mohd.

    In: Acoustical Science and Technology, Vol. 33, No. 2, 2012, p. 111-112.

    Research output: Contribution to journalArticle

    Mustafa, Rahimie ; Ali, Mohd Alauddin Mohd. / Fast and efficient least-mean-squares algorithm for active noise control system identification. In: Acoustical Science and Technology. 2012 ; Vol. 33, No. 2. pp. 111-112.
    @article{8ab5d535f1fa4057a9c11ed50852cec5,
    title = "Fast and efficient least-mean-squares algorithm for active noise control system identification",
    abstract = "A fast and efficient least-mean-squares algorithm for active noise control system identification is presented. The algorithm is incorporated into field programmable gate arrays (FPGA) to ensure real-time and fast convergence. Adaptive system identification for ANC headset application is carried out to validate the algorithm effectiveness. A 64-tap FIR-based LMS adaptive filter is designed on the basis of the multiplier-adder structure and is synthesized onto the Cyclone II FPGA of the Altera DE2 development board. The headset used is the HD280 model from Sennheiser and has been modified by inserting a small microphone through the earmuff. In this experiment, a mannequin wearing the headset is used to replicate a human being. The result shows that in a time-varying situation, repeated weight updates in every audio sample will guarantee error convergence within the period of audio sampling itself.",
    keywords = "Active noise control, Fast convergence, Least-mean-squares, System identification",
    author = "Rahimie Mustafa and Ali, {Mohd Alauddin Mohd}",
    year = "2012",
    doi = "10.1250/ast.33.111",
    language = "English",
    volume = "33",
    pages = "111--112",
    journal = "Acoustical Science and Technology",
    issn = "1346-3969",
    publisher = "Acoustical Society of Japan",
    number = "2",

    }

    TY - JOUR

    T1 - Fast and efficient least-mean-squares algorithm for active noise control system identification

    AU - Mustafa, Rahimie

    AU - Ali, Mohd Alauddin Mohd

    PY - 2012

    Y1 - 2012

    N2 - A fast and efficient least-mean-squares algorithm for active noise control system identification is presented. The algorithm is incorporated into field programmable gate arrays (FPGA) to ensure real-time and fast convergence. Adaptive system identification for ANC headset application is carried out to validate the algorithm effectiveness. A 64-tap FIR-based LMS adaptive filter is designed on the basis of the multiplier-adder structure and is synthesized onto the Cyclone II FPGA of the Altera DE2 development board. The headset used is the HD280 model from Sennheiser and has been modified by inserting a small microphone through the earmuff. In this experiment, a mannequin wearing the headset is used to replicate a human being. The result shows that in a time-varying situation, repeated weight updates in every audio sample will guarantee error convergence within the period of audio sampling itself.

    AB - A fast and efficient least-mean-squares algorithm for active noise control system identification is presented. The algorithm is incorporated into field programmable gate arrays (FPGA) to ensure real-time and fast convergence. Adaptive system identification for ANC headset application is carried out to validate the algorithm effectiveness. A 64-tap FIR-based LMS adaptive filter is designed on the basis of the multiplier-adder structure and is synthesized onto the Cyclone II FPGA of the Altera DE2 development board. The headset used is the HD280 model from Sennheiser and has been modified by inserting a small microphone through the earmuff. In this experiment, a mannequin wearing the headset is used to replicate a human being. The result shows that in a time-varying situation, repeated weight updates in every audio sample will guarantee error convergence within the period of audio sampling itself.

    KW - Active noise control

    KW - Fast convergence

    KW - Least-mean-squares

    KW - System identification

    UR - http://www.scopus.com/inward/record.url?scp=84858253065&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84858253065&partnerID=8YFLogxK

    U2 - 10.1250/ast.33.111

    DO - 10.1250/ast.33.111

    M3 - Article

    VL - 33

    SP - 111

    EP - 112

    JO - Acoustical Science and Technology

    JF - Acoustical Science and Technology

    SN - 1346-3969

    IS - 2

    ER -