Bayesian Regularization and Nonnegative Deconvolution for Time Delay Estimation

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General Robotics, Automation, Sensing and Perception Laboratory
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Lin, Yuanqing
Lee, Daniel D
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Bayesian Regularization and Nonnegative Deconvolution (BRAND) is proposed for estimating time delays of acoustic signals in reverberant environments. Sparsity of the nonnegative filter coefficients is enforced using an L1-norm regularization. A probabilistic generative model is used to simultaneously estimate the regularization parameters and filter coefficients from the signal data. Iterative update rules are derived under a Bayesian framework using the Expectation-Maximization procedure. The resulting time delay estimation algorithm is demonstrated on noisy acoustic data.

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2004-12-13
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2023-05-16T22:28:35.000
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Copyright MIT Press. Postprint version. Published in Advances in Neural Information Processing Systems, Volume 17, pages 809-816.
Copyright MIT Press. Postprint version. Published in Advances in Neural Information Processing Systems, Volume 17, Issue 6, December 2004, pages 809-816.
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