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Bayesian Interpolation and Parameter Estimation in a Dynamic Sinusoidal Model

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5 Author(s)
Nielsen, J.K. ; Dept. of Electron. Syst., Aalborg Univ., Aalborg, Denmark ; Christensen, M.G. ; Cemgil, A.T. ; Godsill, S.J.
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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.

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Audio, Speech, and Language Processing, IEEE Transactions on  (Volume:19 ,  Issue: 7 )