Performance Analysis on Greedy Reconstruction Algorithms for Audio Signals Based on Compressed Sensing

摘要

Compressed Sensing (CS) is a new framework to measure a signal at sub-Nyquist rate, implying that fewer measurements are needed in reduction of acquisition time, simplification of hardware, reduction of memory space required, etc. In this paper, We demonstrate that ideas from compressed sensing may be exploited in the context of real, no-spare audio signals with short-term stability and focus on two types of greedy reconstruction algorithms: matching pursuit and gradient pursuit, including MP, OMP, StOMP, CoSaMP, GP, CGP, etc. The performance of the greedy reconstruction algorithms is analyzed for original audio signals with the reconstruction time, reconstruction error, SDR, etc. It is demonstrated that the compressed sensing framework may be applied with relatively minor modifications for real audio signals with short-term stability.