Blind System Identification Using Sparse Learning for TDOA Estimation of Room Reflections

Abstract

Localization of early room reflections can be achieved by estimating the time-differences-of-arrival (TDOAs) of reflected waves between elements of a microphone array. For an unknown source, we propose to apply sparse blind system identification (BSI) methods to identify the acoustic impulse responses, from which the TDOAs of temporally sparse reflections are estimated. The proposed time- and frequency-domain adaptive algorithms based on crossrelation formulation are regularized by incorporating an l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm sparseness constraint, which is realized using a split Bregman method. These algorithms are shown to outperform standard crossrelation-based BSI techniques when estimating TDOAs of reflections in the presence of background noise.