InforLorV4, Main, Curation, bibRecord, 000E40

Kernel Additive Models for Source Separation

Identifieur interne : 000E40 ( Main/Curation ); précédent : 000E39; suivant : 000E41

Kernel Additive Models for Source Separation

Auteurs : Antoine Liutkus [France] ; Derry Fitzgerald [Irlande (pays)] ; Zafar Rafii [États-Unis] ; Bryan Pardo [États-Unis] ; Laurent Daudet [France]

Source :

IEEE transactions on signal processing [ 1053-587X ] ; 2014.

RBID : Pascal:14-0219788

Descripteurs français

Pascal (Inist)
- Méthode noyau, Séparation source, Estimation paramètre, Modélisation, Signal échantillonné, Détection cible, Localisation source, Autosimilitude, Factorisation, Méthode itérative, Algorithme, Signal audio, Evaluation performance, Traitement signal, Estimation signal.

English descriptors

KwdEn :
- Algorithm, Audio signal, Factorization, Iterative method, Kernel method, Modeling, Parameter estimation, Performance evaluation, Sampled signal, Selfsimilarity, Signal estimation, Signal processing, Source localization, Source separation, Target detection.
mix :
- audio, kernel models, machine learning, 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 non-negative 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, and self-similarity. 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.

Url:

https://hal.inria.fr/hal-01011044

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Le document en format XML

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<front><div type="abstract" xml:lang="en">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 non-negative 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, and self-similarity. 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.</div>
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<term>Audio signal</term>
<term>Factorization</term>
<term>Iterative method</term>
<term>Kernel method</term>
<term>Modeling</term>
<term>Parameter estimation</term>
<term>Performance evaluation</term>
<term>Sampled signal</term>
<term>Selfsimilarity</term>
<term>Signal estimation</term>
<term>Signal processing</term>
<term>Source localization</term>
<term>Source separation</term>
<term>Target detection</term>
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<keywords scheme="Pascal" xml:lang="fr"><term>Méthode noyau</term>
<term>Séparation source</term>
<term>Estimation paramètre</term>
<term>Modélisation</term>
<term>Signal échantillonné</term>
<term>Détection cible</term>
<term>Localisation source</term>
<term>Autosimilitude</term>
<term>Factorisation</term>
<term>Méthode itérative</term>
<term>Algorithme</term>
<term>Signal audio</term>
<term>Evaluation performance</term>
<term>Traitement signal</term>
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<front><div type="abstract" xml:lang="en">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 non-negative 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, and self-similarity. 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.</div>
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<profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>audio</term>
<term>kernel models</term>
<term>machine learning</term>
<term>source separation</term>
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<front><div type="abstract" xml:lang="en">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.</div>
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Kernel Additive Models for Source Separation

Kernel Additive Models for Source Separation

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