Orthogonalization of the Sensing Matrix Through Dominant Columns in Compressive Sensing for Speech Enhancement

Abstract

This paper introduces a novel speech enhancement approach called dominant columns group orthogonalization of the sensing matrix (DCGOSM) in compressive sensing (CS). DCGOSM optimizes the sensing matrix using particle swarm optimization (PSO), ensuring separate basis vectors for speech and noise signals. By utilizing an orthogonal matching pursuit (OMP) based CS signal reconstruction with this optimized matrix, noise components are effectively avoided, resulting in lower noise in the reconstructed signal. The reconstruction process is accelerated by iterating only through the known speech-contributing columns. DCGOSM is evaluated against various noise types using speech quality measures such as SNR, SSNR, STOI, and PESQ. Compared to other OMP-based CS algorithms and deep neural network (DNN)-based speech enhancement techniques, DCGOSM demonstrates significant improvements, with maximum enhancements of 42.54%, 62.97%, 27.48%, and 8.72% for SNR, SSNR, PESQ, and STOI, respectively. Additionally, DCGOSM outperforms DNN-based techniques by 20.32% for PESQ and 8.29% for STOI. Furthermore, it reduces recovery time by at least 13.2% compared to other OMP-based CS algorithms.