Anzeige
Erschienen in:
2012 | OriginalPaper | Buchkapitel
Some Uniqueness Results in Sparse Convolutive Source Separation
verfasst von : Alexis Benichoux, Prasad Sudhakar, Fréderic Bimbot, Rémi Gribonval
Erschienen in: Latent Variable Analysis and Signal Separation
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
The fundamental problems in the traditional frequency domain approaches to convolutive blind source separation are 1) arbitrary permutations and 2) arbitrary scaling in each frequency bin of the estimated filters or sources. These ambiguities are corrected by taking into account some specific properties of the filters or sources, or both. This paper focusses on the filter permutation problem, assuming the absence of the scaling ambiguity, investigating the use of temporal sparsity of the filters as a property to aid permutation correction. Theoretical and experimental results bring out the potential as well as the extent to which sparsity can be used as a hypothesis to formulate a well posed permutation problem.