Speech reconstruction via sparse representation using harmonic regularization

In this paper, we consider a speech reconstruction problem, which is efficiently solved via sparse representation. Though a variety of speech reconstruction methods based on the sparse representation are developed, they seldom take into account the intrinsic attributes of speech, e.g., harmonic structures. To address this issue, a harmonic-based sparse representation algorithm is proposed to emphasize harmonic correlations between the adjacent speech frames. Sequently, a corresponding sparse optimal model is presented with a harmonic regularization term, which can be efficiently solved in an iterative framework. Simulation results demonstrate that the proposed method can achieve a better performance to improve the quality of the reconstructed speech than other traditional sparse representation methods.