Maximum Feasible Subsystem Algorithms for Recovery of Compressively Sensed Speech

Abstract

The goal in signal compression is to reduce the size of the input signal without a significant loss in the quality of the recovered signal. One way to achieve this goal is to apply the principles of compressive sensing, but this has not been particularly successful for real-world signals that are insufficiently sparse, such as speech. We present three new algorithms based on solutions for the MAXimum Feasible Subsystem problem (MAX FS) that improve upon the state of the art in recovery of compressed speech signals: more highly compressed signals can be successfully recovered with greater quality. The new recovery algorithms deliver sparser solutions when compared with those obtained using traditional compressive sensing recovery algorithms. When tested by recovering compressively sensed speech signals from the TIMIT speech database, the recovered speech signals had better perceptual quality than speech signals recovered using traditional compressive sensing recovery algorithms. INDEX TERMS Compressive sensing, maximum feasible subsystem problem, sparse recovery.