Wideband sparse Bayesian learning for off-grid binaural sound source localization

Some traditional binaural sound source localization (BSSL) techniques estimate the azimuth using measured head-related transfer function (HRTF) databases, which is constrained by the discretely measured azimuths of HRTF databases. The azimuth localization performance of these HRTF-based BSSL methods may degrade significantly when the true azimuth is not included in the discretely measured azimuths, which is a typical off-grid problem. This paper proposes an off-grid BSSL method based on an off-grid wideband sparse Bayesian learning algorithm. An off-grid binaural sparse signal model is established first, which takes into account both the shadowing effects by the head and the impacts of off-grid problem. Based on the spatial sparsity of sound sources, the off-grid BSSL problem can be reduced to a convex optimization problem. An off-grid wideband sparse Bayesian learning algorithm is further derived to solve the convex optimization problem and thus improve the localization performance. Experimental results demonstrate that the proposed off-grid BSSL method can achieve higher localization accuracy than the state-of-the-art HRTF-based BSSL methods in various acoustic environments, especially in the off-grid situations. (C) 2019 Elsevier B.V. All rights reserved.