Robust sparsity-promoting acoustic multi-channel equalization for speech dereverberation

This paper presents a novel signal-dependent method to increase the robustness of acoustic multi-channel equalization techniques against room impulse response (RIR) estimation errors. Aiming at obtaining an output signal which better resembles a clean speech signal, we propose to extend the acoustic multi-channel equalization cost function with a penalty function which promotes sparsity of the output signal in the short-time Fourier transform domain. Two conventionally used sparsity-promoting penalty functions are investigated, i.e., the l0-norm and the l1-norm, and the sparsity-promoting filters are iteratively computed using the alternating direction method of multipliers. Simulation results for several RIR estimation errors show that incorporating a sparsity-promoting penalty function significantly increases the robustness, with the l1-norm penalty function outperforming the l0-norm penalty function.