Compressed Domain Packet Loss Concealment of Sinusoidally Coded Speech

In this paper we consider the problem of packet loss concealment for Voice over IP (VoIP). The speech signal is compressed at the transmitter using A sinusoidal coding scheme working at 8 kbit/s. At the receiver, packet loss concealment is carried out working directly on the quantized sinusoidal parameters, based on time-scaling of the packets surrounding the missing ones. Subjective listening tests show promising results indicating the potential of sinusoidal speech coding for VoIP

Bayesian Interpolation and Parameter Estimation in a Dynamic Sinusoidal Model

In this paper, we propose a method for restoring the missing or corrupted observations of nonstationary sinusoidal signals which are often encountered in music and speech applications. To model nonstationary signals, we use a time-varying sinusoidal model which is obtained by extending the static sinusoidal model into a dynamic sinusoidal model. In this model, the in-phase and quadrature components of the sinusoids are modeled as first-order Gauss–Markov processes. The inference scheme for the model parameters and missing observations is formulated in a Bayesian framework and is based on a Markov chain Monte Carlo method known as Gibbs sampler. We focus on the parameter estimation in the dynamic sinusoidal model since this constitutes the core of model-based interpolation. In the simulations, we first investigate the applicability of the model and then demonstrate the inference scheme by applying it to the restoration of lost audio packets on a packet-based network. The results show that the proposed method is a reasonable inference scheme for estimating unknown signal parameters and interpolating gaps consisting of missing/corrupted signal segments

Sparsity in Linear Predictive Coding of Speech

nrpages: 197status: publishe