Informed Generalized Sidelobe Canceler Utilizing Sparsity of Speech Signals
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F13%3A%230002872" target="_blank" >RIV/46747885:24220/13:#0002872 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6661967" target="_blank" >http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6661967</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/MLSP.2013.6661967" target="_blank" >10.1109/MLSP.2013.6661967</a>
Alternative languages
Result language
angličtina
Original language name
Informed Generalized Sidelobe Canceler Utilizing Sparsity of Speech Signals
Original language description
This report proposes a novel variant of the generalized sidelobe canceler. It assumes that a set of prepared relative transfer functions (RTFs) is available for several potential positions of a target source within a confined area. The key problem here is to select the correct RTF at any time, even when the exact position of the target is unknown and interfering sources are present. We propose to select the RTF based on lp-norm, measured at the blocking matrix output in the frequency domain. Subsequentexperiments show that this approach significantly outperforms previously proposed methods for selection when the target and interferer signals are speech signals.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
JC - Computer hardware and software
OECD FORD branch
—
Result continuities
Project
<a href="/en/project/GAP103%2F11%2F1947" target="_blank" >GAP103/11/1947: Methods of Latent Variable Analysis in Blind Speech and Audio Processing</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2013 16th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2013
ISBN
978-1-4799-1180-6
ISSN
2161-0363
e-ISSN
—
Number of pages
6
Pages from-to
—
Publisher name
IEEE Signal Processing Society
Place of publication
—
Event location
Southampton; United Kingdom
Event date
Jan 1, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—