Christensen, Mads Græsbøll

Jensen, Jesper Rindom

Jensen, Søren Holdt

English

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

Nonlinear Least Squares Methods for Joint DOA and Pitch Estimation

Jesper Rindom Jensen

Mads Græsbøll Christensen

Søren Holdt Jensen

Crossref

IEEE Transactions on Audio Speech and Language Processing

https://vbn.aau.dk/ws/files/73856400/Jensen_et_al_2013_twocol.pdf

Nonlinear Least Squares Methods for Joint DOA and Pitch Estimation

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