An overview of informed audio source separation
Identifieur interne : 001389 ( Main/Exploration ); précédent : 001388; suivant : 001390An overview of informed audio source separation
Auteurs : Antoine Liutkus [France] ; Jean-Louis Durrieu [Suisse] ; Laurent Daudet [France] ; Gaël Richard [France]Source :
English descriptors
Abstract
Audio source separation consists in recovering different unknown signals called sources by filtering their observed mixtures. In music processing, most mixtures are stereophonic songs and the sources are the individual signals played by the instruments, e.g. bass, vocals, guitar, etc. Source separation is often achieved through a classical generalized Wiener filtering, which is controlled by parameters such as the power spectrograms and the spatial locations of the sources. For an efficient filtering, those parameters need to be available and their estimation is the main challenge faced by separation algorithms. In the blind scenario, only the mixtures are available and performance strongly depends on the mixtures considered. In recent years, much research has focused on informed separation, which consists in using additional available information about the sources to improve the separation quality. In this paper, we review some recent trends in this direction.
Url:
DOI: 10.1109/WIAMIS.2013.6616139
Affiliations:
- France, Suisse
- Canton de Vaud, Grand Est, Lorraine (région)
- Lausanne, Metz, Nancy
- Université de Lorraine, École polytechnique fédérale de Lausanne
Links toward previous steps (curation, corpus...)
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Le document en format XML
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source separation</term><term>source separation</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Audio source separation consists in recovering different unknown signals called sources by filtering their observed mixtures. In music processing, most mixtures are stereophonic songs and the sources are the individual signals played by the instruments, e.g. bass, vocals, guitar, etc. Source separation is often achieved through a classical generalized Wiener filtering, which is controlled by parameters such as the power spectrograms and the spatial locations of the sources. For an efficient filtering, those parameters need to be available and their estimation is the main challenge faced by separation algorithms. In the blind scenario, only the mixtures are available and performance strongly depends on the mixtures considered. In recent years, much research has focused on informed separation, which consists in using additional available information about the sources to improve the separation quality. In this paper, we review some recent trends in this direction.</div></front></TEI><affiliations><list><country><li>France</li><li>Suisse</li></country><region><li>Canton de Vaud</li><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Lausanne</li><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li><li>École polytechnique fédérale de Lausanne</li></orgName></list><tree><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><name sortKey="Richard, Gael" sort="Richard, Gael" uniqKey="Richard G" first="Gaël" last="Richard">Gaël Richard</name></country><country name="Suisse"><region name="Canton de Vaud"><name sortKey="Durrieu, Jean Louis" sort="Durrieu, Jean Louis" uniqKey="Durrieu J" first="Jean-Louis" last="Durrieu">Jean-Louis Durrieu</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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|wiki= Wicri/Lorraine
|area= InforLorV4
|flux= Main
|étape= Exploration
|type= RBID
|clé= Hal:hal-00958661
|texte= An overview of informed audio source separation
}}
| This area was generated with Dilib version V0.6.33. |  |