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Restoration of Missing Samples in Digital Audio Signals

  • Joseph J. K. Ó Ruanaidh
  • William J. Fitzgerald
Part of the Statistics and Computing book series (SCO)

Abstract

The aim of this chapter is to describe a novel method for interpolating autoregressive data. This is applied to the restoration of missing samples in digital audio signals. The section of audio signal in question is modelled as a stationary autoregressive process, and missing samples are imputed using the Gibbs sampler. The corresponding ML and EM algorithm solutions to the problem are developed and discussed, and the results are compared for both real and synthetic data.

Keywords

Expectation Maximization Gibbs Sampler Expectation Maximization Algorithm Conditional Density Data Augmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • Joseph J. K. Ó Ruanaidh
    • 1
  • William J. Fitzgerald
    • 1
  1. 1.Department of EngineeringUniversity of CambridgeCambridgeUK

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