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.
Originalsprog | Engelsk |
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Tidsskrift | I E E E Transactions on Audio, Speech and Language Processing |
Vol/bind | 21 |
Udgave nummer | 5 |
Sider (fra-til) | 923-933 |
ISSN | 1558-7916 |
DOI | |
Status | Udgivet - maj 2013 |
Emneord
- direction-of-arrival estimation
- Fundamental frequency estimation
- Joint estimation
- nonlinear least squares
- Cramer-Rao bound