Performance Evaluation of Compressive Sensing for multifrequency audio Signals with Various Reconstructing Algorithms

Compressive Sensing (CS) is a new approach in signal processing that aims acquiring and compressing signal simultaneously. CS suggests that if a signal is sparse, the original signal can be reconstructed by exploiting a few random measurements using reconstruction algorithms. In this paper, we investigate the performance of Smoothed- l0 norm (SL0) algorithm to reconstruct an audio signal and compare its performance to the two most used algorithms in audio CS: l1-magic and Orthogonal Matching Pursuit (OMP). This study adopts the Modified Discrete Cosine Transform (MDCT) for sparse representation and random Gaussian matrix for measurement matrix. These Algorithms are evaluated using Signal-to-noise ratio (SNR) and computational complexity for different number of measurements. Results show that SL0 algorithm performs better in both reconstruction quality and computational complexity.

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