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music-information-geometry [2013/08/21 15:23] Arshia Cont [Participants] |
music-information-geometry [2013/08/21 15:24] (Version actuelle) Arshia Cont |
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| Information geometry is a recent field of mathematics, in particular of statistical inference, that studies the notions of probability and information by the way of differential geometry. It is an emerging field that brings together various fields such as machine learning, information theory, signal processing, and differential geometry. | Information geometry is a recent field of mathematics, in particular of statistical inference, that studies the notions of probability and information by the way of differential geometry. It is an emerging field that brings together various fields such as machine learning, information theory, signal processing, and differential geometry. | ||
| - | This project aims at introducing the theoretical concepts and notions of information geometry useful in the development of a formal mathematical framework for the manipulation of audio streams. The idea is to provide alternative structures of manipulation, that respect the temporal and probabilistic natures of audio streams more than the usual structures used in audio content analysis applications do. | + | This project, undertaken by the [[:MuTant]] Team-Project, aims at introducing the theoretical concepts and notions of information geometry useful in the development of a formal mathematical framework for the manipulation of audio streams. The idea is to provide alternative structures of manipulation, that respect the temporal and probabilistic natures of audio streams more than the usual structures used in audio content analysis applications do. |
| This formal framework leads to two applicative fields: automatic structure learning as well as audio stream transformation. The first field is part of the general framework of audio content analysis with applications to automatic structure discovery, automatic segmentation, automatic recognition of auditory scenes, etc. Concerning the second field, applications can be found in audio restoration, data encoding and compression, as well as in providing new methods for sound transformation in analysis-synthesis schemes. | This formal framework leads to two applicative fields: automatic structure learning as well as audio stream transformation. The first field is part of the general framework of audio content analysis with applications to automatic structure discovery, automatic segmentation, automatic recognition of auditory scenes, etc. Concerning the second field, applications can be found in audio restoration, data encoding and compression, as well as in providing new methods for sound transformation in analysis-synthesis schemes. | ||