Abstract

Matching Pursuit (MP) aims at finding sparse decompositions of signals over redundant bases of elementary waveforms. Traditionally, MP has been considered too slow an algorithm to be applied to real-life problems with high-dimensional signals. Indeed, in terms of floating points operations, its typical numerical implementations have a complexity of ¢¤£¦¥¨§� © and are associated with impractical runtimes. In this paper, we propose a new architecture which exploits the structure shared by many redundant MP dictionaries, and thus decreases its complexity to ¢¤£¦¥�������¥¨ ©. This architecture is implemented in a new software toolkit, called MPTK (the Matching Pursuit Toolkit), which is able to reach, e.g., ������� � real time for a typical MP analysis scenario applied to a 1 hour long audio track. This substantial acceleration makes it possible, from now on, to explore and apply MP in the framework of real-life, high-dimensional data processing problems. 1.

Citations