Modélisation et synthèse de voix chantée à partir de descripteurs visuels extraits d'images échographiques et optiques des articulateurs

Abstract : This thesis reports newly developed methods which can be applied to extract relevant features from articulator images in rare singing: traditional Corsican and Sardinian polyphonies, Byzantine music, as well as Human Beat Box. We collected data, and modeled these using machine learning methods, specifically novel deep learning methods. We first modelled tongue ultrasound image sequences, carrying relevant articulatory information which would otherwise be difficult to interpret without specialized skills in ultrasound imaging. We developed methods to extract automatically the superior contour of the tongue displayed on ultrasound images. Our tongue contour extraction results are comparable with those obtained in the literature, which could lead to applications in singing pedagogy. Afterwards, we predicted the evolution of the vocal tract filter parameters from sequences of tongue and lip images, first on isolated vowel databases then on traditional Corsican singing. Applying the predicted filter parameters, combined with the development of a vocal source acoustic model exploiting electroglottographic recordings, allowed us to synthesize singing voice excerpts using articulatory images (of tongue and lips) and glottal activity, with results superior to those obtained using existing technics reported in the literature.
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Aurore Jaumard-Hakoun. Modélisation et synthèse de voix chantée à partir de descripteurs visuels extraits d'images échographiques et optiques des articulateurs. Electronique. Université Pierre et Marie Curie - Paris VI, 2016. Français. ⟨NNT : 2016PA066223⟩. ⟨tel-01441742⟩

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