General algorithms for estimating spectrogram and transfer functions of target signal for blind suppression of diffuse noise
Identifieur interne : 001014 ( Main/Exploration ); précédent : 001013; suivant : 001015General algorithms for estimating spectrogram and transfer functions of target signal for blind suppression of diffuse noise
Auteurs : Nobutaka Ito [Japon] ; Emmanuel Vincent [France] ; Nobutaka Ono [Japon] ; Shigeki Sagayama [Japon]Source :
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Abstract
We propose two algorithms for jointly estimating the power spectrogram and the room transfer functions of a target signal in diffuse noise. These estimates can be used to design a multichannel Wiener filter, and thereby separate a target signal from an unknown direction from diffuse noise. We express a diffuse noise model as a subspace of a matrix linear space, which consists of Hermitian matrices instead of Euclidean vectors. This general framework enables the design of new general algorithms applicable to all specific noise models, instead of multiple specific algorithms each applicable to a single model. The more general proposed algorithms resulted in superior noise suppression performance to our previous algorithms in terms of an output signal-to-noise ratio (SNR).
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type="direct"></relation></listRelation><tutelles><tutelle active="#struct-302983" type="direct"><org type="institution" xml:id="struct-302983" status="VALID"><orgName>The University of Tokyo ( Japan)</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>Japon</country></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>Diffuse noise</term><term>microphone arrays</term><term>multichannel Wiener filter</term><term>noise suppression</term><term>speech enhancement</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We propose two algorithms for jointly estimating the power spectrogram and the room transfer functions of a target signal in diffuse noise. These estimates can be used to design a multichannel Wiener filter, and thereby separate a target signal from an unknown direction from diffuse noise. We express a diffuse noise model as a subspace of a matrix linear space, which consists of Hermitian matrices instead of Euclidean vectors. This general framework enables the design of new general algorithms applicable to all specific noise models, instead of multiple specific algorithms each applicable to a single model. The more general proposed algorithms resulted in superior noise suppression performance to our previous algorithms in terms of an output signal-to-noise ratio (SNR).</div></front></TEI><affiliations><list><country><li>France</li><li>Japon</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="Japon"><noRegion><name sortKey="Ito, Nobutaka" sort="Ito, Nobutaka" uniqKey="Ito N" first="Nobutaka" last="Ito">Nobutaka Ito</name></noRegion><name sortKey="Ono, Nobutaka" sort="Ono, Nobutaka" uniqKey="Ono N" first="Nobutaka" last="Ono">Nobutaka Ono</name><name sortKey="Sagayama, Shigeki" sort="Sagayama, Shigeki" uniqKey="Sagayama S" first="Shigeki" last="Sagayama">Shigeki Sagayama</name></country><country name="France"><region name="Grand Est"><name sortKey="Vincent, Emmanuel" sort="Vincent, Emmanuel" uniqKey="Vincent E" first="Emmanuel" last="Vincent">Emmanuel Vincent</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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