Manuscript due: Dec. 1, 2014

Journal: Speech Communication

Description:

Sparse and low-rank modeling aim to incorporate the low-dimensional
structures pertained to the geometry of the underlying problems to
achieve the optimal solution. These concepts have been proven to be very
effective for a wide range of applications at the intersection of
multiple fields, including machine learning, signal processing and
statistics.

In the context of audio and speech processing, and more particularly
multiparty communications in reverberant and overlapping conditions, the
integration of sparse and low-rank modeling concepts has lead to several
interesting new directions and promising results in speech communication
problems, ranging from denoising to deconvolution and from separation to
recognition. Several other exciting developments include sparse linear
prediction, missing data recovery, audio content analysis and
inpainting. Addressing such real applications is particularly
challenging due to the complex acoustic and speech characteristics, and
the need to develop new modeling strategies that meet the foundational
theoretical hypotheses. In addition, speech recognition performance
seems to degrade in these complex acoustic conditions, and thus research
in this direction is critical from both a theoretical and industry
perspective.

The goal of the proposed special issue is to consolidate the research in
these diverse fields in a coherent framework and overview the recent
advances and trends where sparse and low-rank modeling and applications
are converging to new fundamental and practical paradigms that could
also lead to the emergence of new speech technologies.



Topics of interest include:

* Manifold learning in speech processing: single and multi-microphone
speech enhancement
* Sparse modeling and low-rank modeling for separation and denoising
* Sparse regression and classification
* Sparse dimensionality reduction for feature extraction
* Structured sparsity models underlying audio and speech representation
* Auditory-inspired sparse modeling
* Sparse modeling and low-rank modeling for source localization
* Sparse representation and low-rank representation for reverberant
acoustic modeling
* Sparse data processing and modeling in low-resourced languages
* Applications in speech recognition, privacy-preserving speech
processing, speaker recognition and authentication, speaker diarization,
microphone array calibration, audio information retrieval, speech
synthesis and coding

Lead Guest Editors:

* Prof. Hervé Bourlard, herve.bourlard@idiap.ch
* Dr. Afsaneh Asaei, afsaneh.asaei@idiap.ch
* Dr. Tara N. Sainath, tsainath@us.ibm.com
* Prof. Sharon Gannot, sharon.gannot@biu.ac.il

For more information about this special issue, please visit:

http://si.eurasip.org/issues/36/advances-in-sparse-modeling-and-low-rank-modeling/
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