Guest Editorial: Machine Learning for Signal Processing

Signal processing including analysis, understanding, detection, estimation, and modelling of the events and trends, the way they evolve, and the abnormalities and anomalies affecting them have attracted many researchers around the globe. Signal processing theory originates from mathematical foundation with astonishing applications which help information technologists discover and invent new realities branching off into communications, acoustics, speech, music, biomedical engineering, networking, control, and many other fronts in research and development. A remarkable balance between theory and applications of signal processing has been found with its enormous footprints. Linear algebra, data transforms, and signal distributions have been perhaps playing the major roles in most of these applications. On the other hand, the pioneering works in artificial neural networks, inspired by the structure of central nervous system, by Warren McCulloch and Walter Pitts in 1940’s was another celebrated establishment in the area of data assessment and machine learning. Machine learning prefers to create generativemodels for the problem under study. Inference models and parameters, inherently relying on Bayesian learning, are determined by the data and their environments. Machine Learning and information theoretic ideas can help statistical signal processing overcome the barriers of linear models, and mitigate the need for Gaussianity and stationarity assumptions. Statistical signal processing and inductive inference algorithms provide a common ground at the overlap between signal processing and machine learning which result in some elegant areas of research such as adaptive and nonlinear signal processing, intelligent systems, and multitask cooperative networking. Audio and video processing, brain computer interfacing, self-organized and cognitive information systems are very few out of many application domains of joint machine learning and signal processing systems. We may further categorize the areas where signal processing and machine learning meet as learning theory and techniques; graphical models and kernel methods; datadriven adaptive systems and models; pattern recognition and classification; distributed, Bayesian, subspace/ manifold and sparsity-aware learning; multi-set data analysis and multimodal data fusion; perceptual signal processing in audio, image and video; cognitive information processing; multichannel adaptive and nonlinear signal processing, and their vast applications, including: speech and audio, image and video, music; biomedical signals and images; communications; bioinformatics; biometrics; computational intelligence; genomic signals and sequences; social networks; games, and smart grid. In addition, learning algorithms inherently involve in S. Sanei (*) :A. T. S. Ho Department of Computing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey GU2 7XH, UK e-mail: [email protected]