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

This thesis examines sparse statistical modeling on a range of applications in audio modeling, audio localizations, DNA sequencing, and spectroscopy. In the examined cases, the resulting estimation problems are computationally cumbersome, both as one often suffers from a lack of model order knowledge for this form of problems, but also due to the high dimensionality of the parameter spaces, which typically also yield optimization problems with numerous local minima. In this thesis, these problems are treated using sparse modeling heuristics, with the resulting criteria being solved using convex relaxations, inspired from disciplined convex programming ideas, to maintain tractability. The contributions to audio modeling and estimation focus on the estimation of the fundamental frequency of harmonically ... toggle 9 keywords

parameter estimation – sparse models – convex optimization – fundamental frequency – inharmonicity – audio localization – symbolic periodicity – alternating directions method of multipliers – n-dimensional decaying sinusoids.

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