Scalable audio separation with light kernel additive modelling
Identifieur interne : 000573 ( Main/Exploration ); précédent : 000572; suivant : 000574Scalable audio separation with light kernel additive modelling
Auteurs : Antoine Liutkus [France] ; Derry Fitzgerald [Irlande (pays)] ; Zafar Rafii [États-Unis]Source :
English descriptors
Abstract
Recently, Kernel Additive Modelling (KAM) was proposed as a unified framework to achieve multichannel audio source separation. Its main feature is to use kernel models for locally describing the spectrograms of the sources. Such kernels can capture source features such as repetitivity, stability over time and/or frequency, self-similarity, etc. KAM notably subsumes many popular and effective methods from the state of the art, including REPET and harmonic/percussive separation with median filters. However, it also comes with an important drawback in its initial form: its memory usage badly scales with the number of sources. Indeed, KAM requires the storage of the full-resolution spectrogram for each source, which may become prohibitive for full-length tracks or many sources. In this paper, we show how it can be combined with a fast compression algorithm of its parameters to address the scalability issue, thus enabling its use on small platforms or mobile devices.
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Its main feature is to use kernel models for locally describing the spectrograms of the sources. Such kernels can capture source features such as repetitivity, stability over time and/or frequency, self-similarity, etc. KAM notably subsumes many popular and effective methods from the state of the art, including REPET and harmonic/percussive separation with median filters. However, it also comes with an important drawback in its initial form: its memory usage badly scales with the number of sources. Indeed, KAM requires the storage of the full-resolution spectrogram for each source, which may become prohibitive for full-length tracks or many sources. In this paper, we show how it can be combined with a fast compression algorithm of its parameters to address the scalability issue, thus enabling its use on small platforms or mobile devices.</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>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</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></country><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="États-Unis"><noRegion><name sortKey="Rafii, Zafar" sort="Rafii, Zafar" uniqKey="Rafii Z" first="Zafar" last="Rafii">Zafar Rafii</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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