Abstract / truncated to 115 words (read the full abstract)

Damage to audio signals is in practice common, yet undesirable. Information loss can occur due to improper recording (low sample rate or dynamic range), transmission error (sample dropout), media damage, or because of noise. The removal of such disturbances is possible using inverse problems. Specifically, this work focuses on the situation where sections of an audio signal of length in the order of tens of milliseconds are completely lost, and the goal is to interpolate the missing samples based on the unimpaired context and a suitable signal model. The first part of the dissertation is devoted to convex and non-convex optimization methods, which are designed to find a solution to the interpolation problem based on ... toggle 8 keywords

audio inpainting – audio interpolation – inverse problems – non-negative matrix factorization – optimization – proximal algorithms – restoration – sparsity

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