COMPRESSIVE SENSING IN SPEECH FROM LPC USING GRADIENT PROJECTION FOR SPARSE RECONSTRUCTION

Abstract

This paper presents compressive sensing technique used for speech reconstruction using Linear predictive coding because the speech is more sparse in LPC. DCT of a speech is taken and the DCT points of sparse speech are thrown away arbitrarily. This is achieved by making some point in DCT domain to be zero by multiplying with mask functions. From the incomplete points in DCT domain, the original speech is reconstructed using compressive sensing and the tool used is Gradient Projection for Sparse Reconstruction. The performance of the result is compared with direct IDCT subjectively. The experiment is done and it is observed that the performance is better for compressive sensing than the DCT.