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ISCApad Archive  »  2015  »  ISCApad #205  »  Journals  »  Special issue Speech Communication on Advances in Sparse Modeling and Low-rank Modeling for Speech Processing

ISCApad #205

Wednesday, July 08, 2015 by Chris Wellekens

7-3 Special issue Speech Communication on Advances in Sparse Modeling and Low-rank Modeling for Speech Processing
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, * Dr. Afsaneh Asaei, * Dr. Tara N. Sainath, * Prof. Sharon Gannot, For more information about this special issue, please visit: _______________________________________________

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