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Abstract

In this paper, we consider the problem of joint direction-of-arrival (DOA) and fundamental frequency estimation. Joint estimation enables robust estimation of these parameters in multi-source scenarios where separate estimators may fail. First, we derive the exact and asymptotic Cram\'{e}r-Rao bounds for the joint estimation problem. Then, we propose a nonlinear least squares (NLS) and an approximate NLS (aNLS) estimator for joint DOA and fundamental frequency estimation. The proposed estimators are maximum likelihood estimators when: 1) the noise is white Gaussian, 2) the environment is anechoic, and 3) the source of interest is in the far-field. Otherwise, the methods still approximately yield maximum likelihood estimates. Simulations on synthetic data show that the proposed methods have similar or better performance than state-of-the-art methods for DOA and fundamental frequency estimation. Moreover, simulations on real-life data indicate that the NLS and aNLS methods are applicable even when reverberation is present and the noise is not white Gaussian.
Original languageEnglish
JournalI E E E Transactions on Audio, Speech and Language Processing
Volume21
Issue number5
Pages (from-to)923-933
ISSN1558-7916
DOIs
Publication statusPublished - May 2013

Keywords

    Cite this

    @article{7a1afd1d068840bdb8fa54ae5ae95205,
    title = "Nonlinear Least Squares Methods for Joint DOA and Pitch Estimation",
    abstract = "In this paper, we consider the problem of joint direction-of-arrival (DOA) and fundamental frequency estimation. Joint estimation enables robust estimation of these parameters in multi-source scenarios where separate estimators may fail. First, we derive the exact and asymptotic Cram\'{e}r-Rao bounds for the joint estimation problem. Then, we propose a nonlinear least squares (NLS) and an approximate NLS (aNLS) estimator for joint DOA and fundamental frequency estimation. The proposed estimators are maximum likelihood estimators when: 1) the noise is white Gaussian, 2) the environment is anechoic, and 3) the source of interest is in the far-field. Otherwise, the methods still approximately yield maximum likelihood estimates. Simulations on synthetic data show that the proposed methods have similar or better performance than state-of-the-art methods for DOA and fundamental frequency estimation. Moreover, simulations on real-life data indicate that the NLS and aNLS methods are applicable even when reverberation is present and the noise is not white Gaussian.",
    keywords = "direction-of-arrival estimation, Fundamental frequency estimation, Joint estimation, nonlinear least squares, Cramer-Rao bound",
    author = "Jensen, {Jesper Rindom} and Christensen, {Mads Gr{\ae}sb{\o}ll} and Jensen, {S{\o}ren Holdt}",
    year = "2013",
    month = "5",
    doi = "10.1109/TASL.2013.2239290",
    language = "English",
    volume = "21",
    pages = "923--933",
    journal = "IEEE/ACM Transactions on Audio, Speech, and Language Processing",
    issn = "2329-9290",
    publisher = "IEEE Signal Processing Society",
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    Nonlinear Least Squares Methods for Joint DOA and Pitch Estimation. / Jensen, Jesper Rindom; Christensen, Mads Græsbøll; Jensen, Søren Holdt.

    In: I E E E Transactions on Audio, Speech and Language Processing, Vol. 21, No. 5, 05.2013, p. 923-933.

    Research output: Contribution to journalJournal articleResearchpeer-review

    TY - JOUR

    T1 - Nonlinear Least Squares Methods for Joint DOA and Pitch Estimation

    AU - Jensen, Jesper Rindom

    AU - Christensen, Mads Græsbøll

    AU - Jensen, Søren Holdt

    PY - 2013/5

    Y1 - 2013/5

    N2 - In this paper, we consider the problem of joint direction-of-arrival (DOA) and fundamental frequency estimation. Joint estimation enables robust estimation of these parameters in multi-source scenarios where separate estimators may fail. First, we derive the exact and asymptotic Cram\'{e}r-Rao bounds for the joint estimation problem. Then, we propose a nonlinear least squares (NLS) and an approximate NLS (aNLS) estimator for joint DOA and fundamental frequency estimation. The proposed estimators are maximum likelihood estimators when: 1) the noise is white Gaussian, 2) the environment is anechoic, and 3) the source of interest is in the far-field. Otherwise, the methods still approximately yield maximum likelihood estimates. Simulations on synthetic data show that the proposed methods have similar or better performance than state-of-the-art methods for DOA and fundamental frequency estimation. Moreover, simulations on real-life data indicate that the NLS and aNLS methods are applicable even when reverberation is present and the noise is not white Gaussian.

    AB - In this paper, we consider the problem of joint direction-of-arrival (DOA) and fundamental frequency estimation. Joint estimation enables robust estimation of these parameters in multi-source scenarios where separate estimators may fail. First, we derive the exact and asymptotic Cram\'{e}r-Rao bounds for the joint estimation problem. Then, we propose a nonlinear least squares (NLS) and an approximate NLS (aNLS) estimator for joint DOA and fundamental frequency estimation. The proposed estimators are maximum likelihood estimators when: 1) the noise is white Gaussian, 2) the environment is anechoic, and 3) the source of interest is in the far-field. Otherwise, the methods still approximately yield maximum likelihood estimates. Simulations on synthetic data show that the proposed methods have similar or better performance than state-of-the-art methods for DOA and fundamental frequency estimation. Moreover, simulations on real-life data indicate that the NLS and aNLS methods are applicable even when reverberation is present and the noise is not white Gaussian.

    KW - direction-of-arrival estimation

    KW - Fundamental frequency estimation

    KW - Joint estimation

    KW - nonlinear least squares

    KW - Cramer-Rao bound

    U2 - 10.1109/TASL.2013.2239290

    DO - 10.1109/TASL.2013.2239290

    M3 - Journal article

    VL - 21

    SP - 923

    EP - 933

    JO - IEEE/ACM Transactions on Audio, Speech, and Language Processing

    JF - IEEE/ACM Transactions on Audio, Speech, and Language Processing

    SN - 2329-9290

    IS - 5

    ER -