VoIP reconstruction via sparse linear prediction

Abstract

With the development of network and speech coding, various applications of transmitting voice over internet protocol (VoIP) come into our lives. Speech reconstruction is required, as the packet loss caused by the real-time constraints transmission protocols results in the loss of speech segments. The paper proposes a novel approach to addressing speech reconstruction problem utilizing a high-order sparse linear predictor. This predictor is able to maintain the high sparsity level involved by the classical speech model as much as possible. Then we adopt differential evolution algorithm, which is arguably one of the most powerful stochastic real-parameter optimization algorithms, to predict the sparse coefficients and missing speech segments simultaneously. Experiments show the superior performance of this new approach in both signal to noise ratio (SNR) and perceptual evaluation of speech quality (PESQ).