Clipping is an essential signal processing operation in many real-time audio applications, yet the use of existing clipping techniques generally has a detrimental effect on the perceived audio signal quality. In this paper, we present a novel multidisciplinary approach to clipping which aims to explicitly minimize the perceptible clipping-induced distortion by embedding a convex optimization criterion and a psychoacoustic model into a frame-based algorithm. The core of this perception-based clipping algorithm consists in solving a convex optimization problem for each time frame in a fast and reliable way. To this end, three different structure-exploiting optimization methods are derived in the common mathematical framework of convex optimization, and corresponding theoretical complexity bounds are provided. From comparative audio quality evaluation experiments, it is concluded that the perception-based clipping algorithm results in significantly higher objective audio quality scores than existing clipping techniques. Moreover, the algorithm is shown to be capable to adhere to real-time deadlines without making a sacrifice in terms of audio quality.

This article addresses the fast solution of a Quadratic Program underlying a Linear Model Predictive Control scheme that generates walking motions. We introduce an algorithm which is tailored to the particular requirements of this problem, and therefore able to solve it efficiently. Different aspects of the algorithm are examined, its computational complexity is presented, and a numerical comparison with an existing state of the art solver is made. The approach presented here, extends to other general problems in a straightforward way.

This article addresses the real-time implementation issues of a model predictive control based walking pattern generation for a humanoid robot. We approximate the multibody dynamic model with a linear discrete time system, and at each step solve a quadratic program in order to keep the output within a predefined set of constraints. The focus is on creating an efficient framework for forming and solving the underlying optimization problem. For that purpose we develop: a) a reliable guess for the active constraints at optimality; b) a fast way of generating an initial feasible point with respect to the set of constraints for each preview interval; c) a variable discretization sampling time. A simple implementation of a standard primal active set algorithm which exploits a "hot start" is used to demonstrate the advantages of the first point, while the latter one is verified using an existing dual solver.