We propose a speech dereverberation method in the time-frequency domain, based on an image deblurring algorithm. A reverberant speech magnitude can be modeled as a convolution of a clean speech with a reverberation filter in time-frequency domain. Then, dereverberation problem can be regarded as that of image deblurring. Therefore, the proposed method estimates the clean speech magnitude in the time-frequency domain by using the fast image deconvolution method with priors on sparsity of the clean speech magnitude gradient and exponentially decaying property of reverberation filters along the time axis. Then, scaling the reverberation speech magnitude by a mask obtained from the estimated clean one performs dereverberation. Experimental results show that the described method can enhance speech.

We present a real-time algorithm that finds the Penetration Depth (PD) between general polygonal models based on iterative and local optimization techniques. Given an in-collision configuration of an object in configuration space, we find an initial collision-free configuration using several methods such as centroid difference, maximally clear configuration, motion coherence, random configuration, and sampling-based search. We project this configuration on to a local contact space using a variant of continuous collision detection algorithm and construct a linear convex cone around the projected configuration. We then formulate a new projection of the in-collision configuration onto the convex cone as a Linear Complementarity Problem (LCP), which we solve using a type of Gauss-Seidel iterative algorithm. We repeat this procedure until a locally optimal PD is obtained. Our algorithm can process complicated models consisting of tens of thousands triangles at interactive rates.