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Design and evaluation of sparse models and algorithms for audio inverse problems

Abstract : Today's challenges in the context of audio and acoustic signal processing inverse problems are multiform. Addressing these problems often requires additional appropriate signal models due to their inherent ill-posedness. This work focuses on designing and evaluating audio reconstruction algorithms. Thus, it shows how various sparse models (analysis, synthesis, plain, structured or “social”) are particularly suited for single or multichannel audio signal reconstruction. The core of this work notably identifies the limits of state-of-the-art methods evaluation for audio declipping and proposes a rigourous large-scale evaluation protocol to determine the more appropriate methods depending on the context (music or speech, moderately or highly degraded signals). Experimental results demonstrate substantial quality improvements for some newly considered testing configurations. We also show computational efficiency of the different methods and considerable speed improvements. Additionally, a part of this work is dedicated to the sound source localization problem. We address it with a “virtually supervised” machine learning technique. Experiments show with this method promising results on distance and direction of arrival estimation.
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Submitted on : Wednesday, June 5, 2019 - 4:06:07 PM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM


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  • HAL Id : tel-02148598, version 1


Clément Gaultier. Design and evaluation of sparse models and algorithms for audio inverse problems. Signal and Image Processing. Université Rennes 1, 2019. English. ⟨NNT : 2019REN1S009⟩. ⟨tel-02148598⟩



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