Room Impulse Response Estimation using Sparse Online Prediction and Absolute Loss
General Robotics, Automation, Sensing and Perception Laboratory
The need to accurately and efficiently estimate room impulse responses arises in many acoustic signal processing applications. In this work, we present a general family of algorithms which contain the conventional normalized least mean squares (NLMS) algorithm as a special case. Specific members of this family yield estimates which are robust both to different noise models and choice of parameters. We demonstrate the merits of our approach to accurately estimate sparse room impulse responses in simulations with speech signals.