Ina Kodrasi

TL;DR: A partial multichannel equalization technique based on MINT (P-MINT) is proposed which aims to shorten the RIR and an automatic non-intrusive procedure for determining the regularization parameter based on the L-curve is introduced.

TL;DR: Experimental results show that the proposed system is effective in suppressing both reverberation and noise while improving the speech quality, and the achieved improvements are particularly significant in conditions with high reverberation times.

TL;DR: A numerical analysis of the suitability of different non-parametric and parametric measures for sparsity characterization shows that kurtosis, the Gini index, and the parametric sparsity measures are advantageous sparsity Measures, whereas the $l_1$-norm and entropy measures fail to robustly characterize the temporal sparsity of signals with a different number of time frames.

TL;DR: Six different single-channel dereverberation algorithms are evaluated subjectively in terms of speech intelligibility and speech quality and the best performance was observed for the regularized spectral inverse approach with pre-echo removal.

TL;DR: Two time-domain techniques aiming at joint dereverberation and noise reduction based on acoustic multi-channel equalization based on multiple-input/output inverse theorem are proposed and it is shown that the MWF-D NR technique yields a significantly better noise reduction performance than the RPM-DNR technique at the expense of a worse derever beration performance.