Bayesian interpolation in a dynamic sinusoidal model with application to packet-loss concealment

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

2 Citationer (Scopus)
98 Downloads (Pure)

Resumé

In this paper, we consider Bayesian interpolation and parameter estimation in a dynamic sinusoidal model. This model is more flexible than the static sinusoidal model since it enables the amplitudes and phases of the sinusoids to be time-varying. For the dynamic sinusoidal model, we derive a Bayesian inference scheme for the missing observations, hidden states and model parameters of the dynamic model. The inference scheme is based on a Markov chain Monte Carlo method known as Gibbs sampler. We illustrate the performance of the inference scheme to the application of packet-loss concealment of lost audio and speech packet
OriginalsprogEngelsk
TidsskriftProceedings of the European Signal Processing Conference
Vol/bind2010
Sider (fra-til)239-243
ISSN2076-1465
StatusUdgivet - 24 aug. 2010
BegivenhedEUSIPCO 2010 - Aalborg, Danmark
Varighed: 23 aug. 2010 → …

Konference

KonferenceEUSIPCO 2010
LandDanmark
ByAalborg
Periode23/08/2010 → …

Fingerprint

Packet loss
Dynamic models
Interpolation
Parameter estimation
Markov processes
Monte Carlo methods

Citer dette

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title = "Bayesian interpolation in a dynamic sinusoidal model with application to packet-loss concealment",
abstract = "In this paper, we consider Bayesian interpolation and parameter estimation in a dynamic sinusoidal model. This model is more flexible than the static sinusoidal model since it enables the amplitudes and phases of the sinusoids to be time-varying. For the dynamic sinusoidal model, we derive a Bayesian inference scheme for the missing observations, hidden states and model parameters of the dynamic model. The inference scheme is based on a Markov chain Monte Carlo method known as Gibbs sampler. We illustrate the performance of the inference scheme to the application of packet-loss concealment of lost audio and speech packet",
author = "Nielsen, {Jesper Kj{\ae}r} and Christensen, {Mads Gr{\ae}sb{\o}ll} and Cemgil, {Ali Taylan} and Godsill, {Simon J.} and Jensen, {S{\o}ren Holdt}",
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Bayesian interpolation in a dynamic sinusoidal model with application to packet-loss concealment. / Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Cemgil, Ali Taylan; Godsill, Simon J.; Jensen, Søren Holdt.

I: Proceedings of the European Signal Processing Conference, Bind 2010, 24.08.2010, s. 239-243.

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

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AU - Cemgil, Ali Taylan

AU - Godsill, Simon J.

AU - Jensen, Søren Holdt

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N2 - In this paper, we consider Bayesian interpolation and parameter estimation in a dynamic sinusoidal model. This model is more flexible than the static sinusoidal model since it enables the amplitudes and phases of the sinusoids to be time-varying. For the dynamic sinusoidal model, we derive a Bayesian inference scheme for the missing observations, hidden states and model parameters of the dynamic model. The inference scheme is based on a Markov chain Monte Carlo method known as Gibbs sampler. We illustrate the performance of the inference scheme to the application of packet-loss concealment of lost audio and speech packet

AB - In this paper, we consider Bayesian interpolation and parameter estimation in a dynamic sinusoidal model. This model is more flexible than the static sinusoidal model since it enables the amplitudes and phases of the sinusoids to be time-varying. For the dynamic sinusoidal model, we derive a Bayesian inference scheme for the missing observations, hidden states and model parameters of the dynamic model. The inference scheme is based on a Markov chain Monte Carlo method known as Gibbs sampler. We illustrate the performance of the inference scheme to the application of packet-loss concealment of lost audio and speech packet

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JO - Proceedings of the European Signal Processing Conference

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