Departmental Papers (ESE)


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.

Document Type

Conference Paper

Subject Area


Date of this Version

December 2004


Copyright MIT Press. Postprint version. Published in Advances in Neural Information Processing Systems, Volume 17, pages 809-816.



Date Posted: 05 August 2005