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
SN - 1558-7916
VL - 21
SP - 923
EP - 933
JO - I E E E Transactions on Audio, Speech and Language Processing
JF - I E E E Transactions on Audio, Speech and Language Processing
IS - 5
ER -