Bayesian Regularization and Nonnegative Deconvolution for Time Delay Estimation
General Robotics, Automation, Sensing and Perception Laboratory
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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Copyright MIT Press. Postprint version. Published in Advances in Neural Information Processing Systems, Volume 17, Issue 6, December 2004, pages 809-816.