Speech Signal Recovery Using Block Sparse Bayesian Learning

Compressed sensing is based on the recovery of original signal from the low-quality and incomplete samples. Recently, \(\ell _1\)-norm is used for the estimation of signal elements from the underdetermined set of equations. In this paper, we propose a technique for speech signal recovery called block sparse Bayesian learning. The proposed technique is applied over the random set of speech samples and acquired better performance as compared to \(\ell _1\)-based recovery. Apart from the proposed recovery technique, this work is also intended to develop a trained and efficient sampling matrix through offline training. In this work, we apply structural similarity index as a metric to compare the performance of the proposed technique with an existing \(\ell _1\) based recovery. Sparse Bayesian learning and \(\ell _1\)-norm recovery are applied over the selected audio files from the datasets. The dataset consists of speech signals from three different languages: Urdu, Pashto and English. Structural similarity between the recovered and original speech signals is used as a metric to compare the performance of BSBL with \(\ell _1\)-norm minimization. The comparison based on structural similarity index shows the effectiveness of the proposed technique.