Emerging Machine Learning Techniques in Signal Processing

Call for Papers

In the era of knowledge-based society and machine automation there is a strong interest in machine learning techniques in a wide range of applications. The attention paid to machine learning methods within the DSP community is not new. Speech recognition is an example of an area where DSP and machine learning “mix” together to develop efficient and robust speech recognizers. Channel equalization is another area at the intersection of machine learning and DSP techniques. After all, deciding upon the transmitted information symbol is nothing but a class assignment task. In cognitive radio, DSP techniques and machine learning methods can work together for developing algorithms for the efficient utilization of the radio spectrum. Image/video/music recognition and retrieval are some more typical examples where SP and machine learning tie together. Another problem at the heart of the DSP community interests is the regression task that can be cast as a machine learning problem.

Over the past years a number of new powerful machine learning techniques have been developed, which are suitable for nonlinear processing and for the general case of non-Gaussian data, and also for nondifferentiable cost functions or cost functions referring to robust statistics. Adaptive versions of some of these powerful techniques have only recently started being studied. This is an area in which the DSP community has a lot to say and contribute.

The focus of this special issue is twofold: (a) to consider novel theoretical results in machine learning methods and algorithms in the light of typical DSP applications and (b) to report novel results obtained by the application of ML techniques in some typical DSP tasks.

The special issue is intended to cover topics such as:

  • Kernel methods (support Vector and Gaussian process machines and their modifications and extensions)
  • Bayesian networks
  • Ensembles: committees, mixtures, boosting, and so forth
  • Adaptive algorithms for machine learning
  • Applications to speech, audio, video, image, communications, and so forth
  • New data representations and learning (independent component analysis, feature selection, feature learning, kernel PCA, etc.)

Authors should follow the EURASIP Journal on Advances in Signal Processing manuscript format described at the journal site http://www.hindawi.com/journals/asp/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/ according to the following timetable:

Manuscript DueOctober 1, 2007
First Round of ReviewsJanuary 1, 2008
Publication DateApril 1, 2008

Guest Editors

  • Theodoros Evgeniou, INSEAD, Boulevard de constance, 77300 Fontainebleau, France
  • Aníbal R. Figueiras-Vidal, Universidad Carlos III de Madrid (UC3M), Avenida Universidad 30, 28911 Leganés, Madrid, Spain
  • Sergios Theodoridis, Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Panepistimiopolis, Ilissia, 15784 Athens, Greece