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Harmonic/Percussive Separation Using Kernel Additive Modelling

Identifieur interne : 000B38 ( Main/Exploration ); précédent : 000B37; suivant : 000B39

Harmonic/Percussive Separation Using Kernel Additive Modelling

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

Source :

RBID : Hal:hal-01000001

English descriptors

Abstract

Recently, Kernel Additive Modelling was proposed as a new framework for performing sound source separation. Kernel Additive Modelling assumes that a source at some location can be estimated using its values at nearby locations where nearness is defined through a source-specific proximity kernel. Different proximity kernels can be used for different sources, which are then separated using an iterative kernel backfitting algorithm. These kernels can efficiently account for features such as continuity, stability in time or frequency and self-similarity. Here, we show that Kernel Additive Modelling can be used to generalise, extend and improve on a widely-used harmonic/percussive separation algorithm which attempts to separate pitched from percussive instruments.

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Le document en format XML

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<desc>
<address>
<country key="FR"></country>
</address>
</desc>
</org>
</tutelle>
</tutelles>
</hal:affiliation>
<country>France</country>
</affiliation>
</author>
</analytic>
</biblStruct>
</sourceDesc>
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<profileDesc>
<textClass>
<keywords scheme="mix" xml:lang="en">
<term>Harmonic/Percussive Separation</term>
<term>Kernel Additive Modelling</term>
<term>Sound Source Separation</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">Recently, Kernel Additive Modelling was proposed as a new framework for performing sound source separation. Kernel Additive Modelling assumes that a source at some location can be estimated using its values at nearby locations where nearness is defined through a source-specific proximity kernel. Different proximity kernels can be used for different sources, which are then separated using an iterative kernel backfitting algorithm. These kernels can efficiently account for features such as continuity, stability in time or frequency and self-similarity. Here, we show that Kernel Additive Modelling can be used to generalise, extend and improve on a widely-used harmonic/percussive separation algorithm which attempts to separate pitched from percussive instruments.</div>
</front>
</TEI>
<affiliations>
<list>
<country>
<li>France</li>
<li>Irlande (pays)</li>
<li>États-Unis</li>
</country>
<region>
<li>Grand Est</li>
<li>Lorraine (région)</li>
</region>
<settlement>
<li>Nancy</li>
</settlement>
<orgName>
<li>Institut national polytechnique de Lorraine</li>
<li>Université Nancy 2</li>
<li>Université de Lorraine</li>
</orgName>
</list>
<tree>
<country name="Irlande (pays)">
<noRegion>
<name sortKey="Fitzgerald, Derry" sort="Fitzgerald, Derry" uniqKey="Fitzgerald D" first="Derry" last="Fitzgerald">Derry Fitzgerald</name>
</noRegion>
</country>
<country name="France">
<region name="Grand Est">
<name sortKey="Liutkus, Antoine" sort="Liutkus, Antoine" uniqKey="Liutkus A" first="Antoine" last="Liutkus">Antoine Liutkus</name>
</region>
<name sortKey="Daudet, Laurent" sort="Daudet, Laurent" uniqKey="Daudet L" first="Laurent" last="Daudet">Laurent Daudet</name>
</country>
<country name="États-Unis">
<noRegion>
<name sortKey="Rafii, Zafar" sort="Rafii, Zafar" uniqKey="Rafii Z" first="Zafar" last="Rafii">Zafar Rafii</name>
</noRegion>
<name sortKey="Pardo, Bryan" sort="Pardo, Bryan" uniqKey="Pardo B" first="Bryan" last="Pardo">Bryan Pardo</name>
</country>
</tree>
</affiliations>
</record>

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