Robust Relative Transfer Function Identification on Manifolds for Speech Enhancement
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F21%3A00008828" target="_blank" >RIV/46747885:24220/21:00008828 - isvavai.cz</a>
Result on the web
<a href="https://asap.ite.tul.cz/wp-content/uploads/sites/3/2021/10/Manifold_Learning_Beamformer.pdf" target="_blank" >https://asap.ite.tul.cz/wp-content/uploads/sites/3/2021/10/Manifold_Learning_Beamformer.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/EUSIPCO54536.2021.9616175" target="_blank" >10.23919/EUSIPCO54536.2021.9616175</a>
Alternative languages
Result language
angličtina
Original language name
Robust Relative Transfer Function Identification on Manifolds for Speech Enhancement
Original language description
Accurate and reliable identification of the relative transfer function (RTF) between microphones with respect to a desired source is an essential component in the design of microphone array beamformers. In this paper, we present a robust RTF identification method on manifolds, tested and trained with real recordings. This method relies on a manifold learning (ML) approach to infer a representation of typical RTFs in a confined area within an acoustic enclosure. We propose a robust supervised identification method that combines the a priori learned geometric structure and the measured signals. A series of experiments using a recently established database of acoustic responses taken at the Bar-Ilan university acoustic lab, demonstrate the effectiveness of the proposed approach over a standard, non-robust, beamforming design method.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
<a href="/en/project/GA20-17720S" target="_blank" >GA20-17720S: Advanced Mixing Models for Blind Source Extraction</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
European Signal Processing Conference (EUSIPCO 2020)
ISBN
978-908279706-0
ISSN
2219-5491
e-ISSN
—
Number of pages
4
Pages from-to
401-405
Publisher name
Eurasip
Place of publication
Ireland
Event location
Dublin
Event date
Jan 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000764066600081