Restoration of Missing Samples in Digital Audio Signals
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.
KeywordsExpectation Maximization Gibbs Sampler Expectation Maximization Algorithm Conditional Density Data Augmentation
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