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FINAL CALL FOR PAPERS (**new deadline **) <br>
<br>
EURASIP Journal on Advances in Signal Processing <br>
<b>Special Issue on Informed Acoustic Source Separation</b><br>
<br>
The complete call of papers is accessible at:<br>
http://asp.eurasipjournals.com/sites/10233/pdf/H9386_DF_CFP_EURASIP_JASP_A4_3.pdf<br>
<br>
DEADLINE: FULL PAPER SUBMISSION: **17th June 2013**<br>
<br>
but we ask the authors of each paper to send by email to the lead
Editor Gaël Richard, <br>
the title, authors list and abstract of their paper (changes will be
possible) by **MAY31st 2013**<br>
<br>
------------------------------------------------------------------------------------------------------<br>
Short Description<br>
<br>
The proposed topic of this special issue is informed acoustic source
separation. As source separation has long become a field of interest
in the signal processing community, recent works increasingly point
out the fact that separation can only be reliably achieved in
real-world use cases when accurate prior information can be
successfully incorporated. Informed separation algorithms can be
characterized by the fact that case-specific prior knowledge is made
available to the algorithm for processing. In this respect, they
contrast with blind methods for which no specific prior information
is available.<br>
Following on the success of the special session on the same topic in
EUSIPCO 2012 at Bucharest, we would like to present recent methods,
discuss the trends and perspectives of this domain and to draw the
attention of the signal processing community to this important
problem and its potential applications. We are interested in both
methodological advances and applications. Topics of interest
include (but are not limited to):<br>
<br>
• Sparse decomposition methods<br>
• Subspace learning methods for sparse decomposition<br>
• Non-negative matrix / tensor factorization<br>
• Robust principal component analysis<br>
• Probabilistic latent component analysis<br>
• Independent component analysis<br>
• Multidimensional component analysis<br>
• Multimodal source separation<br>
• Video-assisted source separation<br>
• Spatial audio object coding <br>
• Reverberant models for source separation<br>
• Score-informed source separation<br>
• Language-informed speech separation<br>
• User-guided source separation<br>
• Source separation informed by cover version<br>
• Informed source separation applied to speech, music or
environmental signals<br>
• …<br>
<br>
-------------------<br>
Guest Editors<br>
Taylan Cemgil, Bogazici University, Turkey, <br>
Tuomas Virtanen, Tampere University of Technology, Finland, <br>
Alexey Ozerov, Technicolor, France, <br>
Derry Fitzgerald, Dublin institute of Technology, Ireland, <br>
<br>
Lead Guest Editor:<br>
Gaël Richard, Institut Mines-Télécom, Télécom ParisTech, CNRS-LTCI,
France,
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