Kernel additive modeling for interference reduction in multi-channel music recordings
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Auteurs : Thomas Pr Tzlich [Allemagne] ; Rachel Bittner [États-Unis] ; Antoine Liutkus [France] ; Meinard Müller [Allemagne]Source :
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Abstract
When recording a live musical performance, the different voices, such as the instrument groups or soloists of an orchestra, are typically recorded in the same room simultaneously, with at least one microphone assigned to each voice. However, it is difficult to acoustically shield the microphones. In practice, each one contains interference from every other voice. In this paper, we aim to reduce these interferences in multi-channel recordings to recover only the isolated voices. Following the recently proposed Kernel Additive Modeling framework, we present a method that iteratively estimates both the power spectral density of each voice and the corresponding strength in each microphone signal. With this information, we build an optimal Wiener filter, strongly reducing interferences. The trade-off between distortion and separation can be controlled by the user through the number of iterations of the algorithm. Furthermore, we present a computationally effective approximation of the iterative procedure. Listening tests demonstrate the effectiveness of the method.
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Erlangen-Nürnberg</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>Allemagne</country></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>audio source separation</term><term>interference reduction</term><term>kernel additive modeling</term><term>multi-channel recordings</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">When recording a live musical performance, the different voices, such as the instrument groups or soloists of an orchestra, are typically recorded in the same room simultaneously, with at least one microphone assigned to each voice. However, it is difficult to acoustically shield the microphones. In practice, each one contains interference from every other voice. In this paper, we aim to reduce these interferences in multi-channel recordings to recover only the isolated voices. Following the recently proposed Kernel Additive Modeling framework, we present a method that iteratively estimates both the power spectral density of each voice and the corresponding strength in each microphone signal. With this information, we build an optimal Wiener filter, strongly reducing interferences. The trade-off between distortion and separation can be controlled by the user through the number of iterations of the algorithm. Furthermore, we present a computationally effective approximation of the iterative procedure. Listening tests demonstrate the effectiveness of the method.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Kernel additive modeling for interference reduction in multi-channel music recordings</title><author role="aut"><persName><forename type="first">Thomas</forename><surname>Prätzlich</surname></persName><email>thoma.praetzlich@audiolabs-erlangen.de</email><ptr type="url" target="http://www.audiolabs-erlangen.de/fau/assistant/praetzlich"></ptr><idno type="halauthor">1128446</idno><affiliation ref="#struct-218524"></affiliation></author><author role="aut"><persName><forename type="first">Rachel</forename><surname>Bittner</surname></persName><email>rachel.bittner@nyu.edu</email><ptr type="url" target="http://steinhardt.nyu.edu/marl/people/bittner"></ptr><idno type="halauthor">1128447</idno><affiliation ref="#struct-412464"></affiliation></author><author role="aut"><persName><forename type="first">Antoine</forename><surname>Liutkus</surname></persName><email></email><idno type="idhal">antoine-liutkus</idno><idno type="halauthor">628142</idno><affiliation ref="#struct-2359"></affiliation><affiliation ref="#struct-420403"></affiliation></author><author role="aut"><persName><forename type="first">Meinard</forename><surname>Müller</surname></persName><email>meinard.mueller@audiolabs-erlangen.de</email><ptr type="url" target="http://www.audiolabs-erlangen.de/fau/professor/mueller"></ptr><idno type="halauthor">1128448</idno><affiliation ref="#struct-218524"></affiliation></author><editor role="depositor"><persName><forename>Antoine</forename><surname>Liutkus</surname></persName><email>antoine@liutkus.fr</email></editor><funder>This work has been partly supported by the BMBF project Freischütz Digital (Funding Code 01UG1239A to C)</funder></titleStmt><editionStmt><edition n="v1"><date type="whenSubmitted">2015-02-13 23:11:47</date></edition><edition n="v2" type="current"><date type="whenSubmitted">2015-02-18 13:05:37</date><date type="whenWritten">2014-09</date><date type="whenModified">2015-11-13 01:08:39</date><date type="whenReleased">2015-02-19 11:01:00</date><date type="whenProduced">2015-04-19</date><date type="whenEndEmbargoed">2015-02-18</date><ref type="file" target="https://hal.inria.fr/hal-01116686v2/document"><date notBefore="2015-02-18"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/hal-01116686/file/2015_PraetzlichBittnerLiutkusMueller_KAMIR_ICASSP.pdf"><date notBefore="2015-02-18"></date></ref></edition><respStmt><resp>contributor</resp><name key="153135"><persName><forename>Antoine</forename><surname>Liutkus</surname></persName><email>antoine@liutkus.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-01116686</idno><idno type="halUri">https://hal.inria.fr/hal-01116686</idno><idno type="halBibtex">pratzlich:hal-01116686</idno><idno type="halRefHtml">IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2015, Brisbane, Australia. 2015</idno><idno type="halRef">IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2015, Brisbane, Australia. 2015</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="INRIA2">INRIA 2</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="INRIA_TEST">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Kernel additive modeling for interference reduction in multi-channel music recordings</title><author role="aut"><persName><forename type="first">Thomas</forename><surname>Prätzlich</surname></persName><email>thoma.praetzlich@audiolabs-erlangen.de</email><ptr type="url" target="http://www.audiolabs-erlangen.de/fau/assistant/praetzlich"></ptr><idno type="halAuthorId">1128446</idno><affiliation ref="#struct-218524"></affiliation></author><author role="aut"><persName><forename type="first">Rachel</forename><surname>Bittner</surname></persName><email>rachel.bittner@nyu.edu</email><ptr type="url" target="http://steinhardt.nyu.edu/marl/people/bittner"></ptr><idno type="halAuthorId">1128447</idno><affiliation ref="#struct-412464"></affiliation></author><author role="aut"><persName><forename type="first">Antoine</forename><surname>Liutkus</surname></persName><idno type="idHal">antoine-liutkus</idno><idno type="halAuthorId">628142</idno><affiliation ref="#struct-2359"></affiliation><affiliation ref="#struct-420403"></affiliation></author><author role="aut"><persName><forename type="first">Meinard</forename><surname>Müller</surname></persName><email>meinard.mueller@audiolabs-erlangen.de</email><ptr type="url" target="http://www.audiolabs-erlangen.de/fau/professor/mueller"></ptr><idno type="halAuthorId">1128448</idno><affiliation ref="#struct-218524"></affiliation></author></analytic><monogr><meeting><title>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</title><date type="start">2015-04-19</date><date type="end">2015-04-24</date><settlement>Brisbane</settlement><country key="AU">Australia</country></meeting><imprint><date type="datePub">2015-02-13</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">multi-channel recordings</term><term xml:lang="en">interference reduction</term><term xml:lang="en">audio source separation</term><term xml:lang="en">kernel additive modeling</term></keywords><classCode scheme="halDomain" n="info.info-ts">Computer Science [cs]/Signal and Image Processing</classCode><classCode scheme="halDomain" n="info.info-ir">Computer Science [cs]/Information Retrieval [cs.IR]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">When recording a live musical performance, the different voices, such as the instrument groups or soloists of an orchestra, are typically recorded in the same room simultaneously, with at least one microphone assigned to each voice. However, it is difficult to acoustically shield the microphones. In practice, each one contains interference from every other voice. In this paper, we aim to reduce these interferences in multi-channel recordings to recover only the isolated voices. Following the recently proposed Kernel Additive Modeling framework, we present a method that iteratively estimates both the power spectral density of each voice and the corresponding strength in each microphone signal. With this information, we build an optimal Wiener filter, strongly reducing interferences. The trade-off between distortion and separation can be controlled by the user through the number of iterations of the algorithm. Furthermore, we present a computationally effective approximation of the iterative procedure. Listening tests demonstrate the effectiveness of the method.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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