Kernel Additive Models for Source Separation

Abstract : Source separation consists of separating a signal into additive components. It is a topic of considerable interest with many applications that has gathered much attention recently. Here, we introduce a new framework for source separation called Kernel Additive Modelling, which is based on local regression and permits efficient separation of multidimensional and/or nonnegative and/or non-regularly sampled signals. The main idea of the method is to assume that a source at some location can be estimated using its values at other locations nearby, where nearness is defined through a source-specific proximity kernel. Such a kernel provides an efficient way to account for features like periodicity, continuity, smoothness, stability over time or frequency, self-similarity, etc. In many cases, such local dynamics are indeed much more natural to assess than any global model such as a tensor factorization. This framework permits one to use different proximity kernels for different sources and to separate them using the iterative kernel backfitting algorithm we describe. As we show, kernel additive modelling generalizes many recent and efficient techniques for source separation and opens the path to creating and combining source models in a principled way. Experimental results on the separation of synthetic and audio signals demonstrate the effectiveness of the approach.
Type de document :
Article dans une revue
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2014, pp.14. 〈10.1109/TSP.2014.2332434〉
Liste complète des métadonnées

Littérature citée [55 références]  Voir  Masquer  Télécharger
Contributeur : Antoine Liutkus <>
Soumis le : lundi 16 février 2015 - 00:31:53
Dernière modification le : vendredi 4 janvier 2019 - 17:33:27
Document(s) archivé(s) le : dimanche 16 avril 2017 - 08:49:43


Fichiers produits par l'(les) auteur(s)



Antoine Liutkus, Derry Fitzgerald, Zafar Rafii, Bryan Pardo, Laurent Daudet. Kernel Additive Models for Source Separation. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2014, pp.14. 〈10.1109/TSP.2014.2332434〉. 〈hal-01011044v2〉



Consultations de la notice


Téléchargements de fichiers