Signal Processing and Speech Communication Laboratory
homeresearch projects › Signal and Information Processing in Science and Engineering - Nonlinear Dynamic and Machine Learning (SISE-NDML-II) FWF S10610-N13

Signal and Information Processing in Science and Engineering - Nonlinear Dynamic and Machine Learning (SISE-NDML-II) FWF S10610-N13

Period
2011 — 2014
Funding
Fonds zur Förderung der wissenschaftlichen Forschung, FWF (Österreich)
Partners
  • Institut für Signalverarbeitung und Sprachkommunikation
Research Areas
Contact

The modeling, measurement, transmission, and processing of information-bearing data and signals are key constituents of any modern technical system. Driven by scalability and reliability considerations, there has recently been a remarkable trend to implement these constituents in a distributed manner. Notable examples for distributed information processing architectures are communication networks, sensor networks, smart grids, traffic telematic systems, and grid computing. The project Signal and Information Processing in Science and Engineering (SISE) aims at making fundamental contributions to some of the most eminent and pressing problems arising in the context of distributed information processing. This ambitious goal requires the development of new mathematical theories, the design and analysis of algorithms and communication protocols, and implementations in hardware and software. The SISE network consists of research groups working in mathematics, signal processing, communications, machine learning, and scientific computing, and hence is perfectly suited to meet the challenges imposed by the multi-disciplinary nature of the project aim.

Related publications
  • Conference contribution Leitner C. & Pernkopf F. (2011) The Pre-Image Problem and Kernel PCA for Speech Enhancement. in ISCA Tutorial and Research Workshop on Non-Linear Speech Processing (pp. 199-206). [more info]
  • Conference contribution Leitner C., Pernkopf F. & Kubin G. (2011) Kernel PCA for Speech Enhancement. in Interspeech - International Conference on Spoken Language Processing (pp. 1221-1224). [more info]
  • Poster Buchgraber T. & Shutin D. (2009) Sparse Bayesian Distributed Least-Squares Strategies for Adaptive Networks.. [more info]