Blind Source Separation

Ganesh R. Naik received B.E. degree in Electronics and communication Engineering from the University of Mysore, India, in 1997. M.E. degree in Communication and Information Engineering from Griffith University, Brisbane, Australia, in 2002 and the PhD degree in the area of Electronics Engineering, specialised in Biomedical Engineering and Signal processing from RMIT University, Melbourne, Australia, in 2009. He is currently an academician and researcher at RMIT University. As an early career researcher, he has edited 6 books, authored more than 75 papers in peer reviewed journals, conferences and book chapters over the last five years. His research interests include Pattern recognition, BSS/ICA techniques, Audio signal processing, Biosignal processing and Human-Computer Interface. Dr. Naik was the Chair for the IEEE Computer Society CIT08 Conference, Sydney and a member of the organising committee for IEEE BRC2011, IEEE BRC 2012 and IEEE BRC 2013 conferences that are held in Brazil. He is also a reviewer and member of editorial board in several reputed journals. He was a recipient of the Baden-Wurttemberg Scholarship from the University of Berufsakademie, Stuttgart, Germany (2006-2007). In 2010, Dr. Naik is awarded with ISSI overseas fellowship from skilled Institute Victoria, Australia. Wenwu Wang was born in Anhui, China, in 1974. He received the B.Sc. degree in automatic control in 1997, the M.E. degree in control science and control engineering in 2000 and the Ph.D. degree in navigation guidance and control in 2002, all from Harbin Engineering University, China. He joined the Department of Electronic Engineering, King's College London, U.K., as a Postdoctoral Research Associate in May 2002, and then transferred to the Cardiff School of Engineering, Cardiff University, U.K., in January 2004. In May 2005, he joined the Tao Group Ltd. (now Antix Labs Ltd.), U.K., as a DSP engineer. In September 2006, he moved to the Creative Technology Ltd. working at the Sensaura Division, U.K., as a Software R&D Engineer. Since May 2007, he has been with the Centre for Vision Speech and Signal Processing, University of Surrey, U.K., where he is currently a Lecturer. He is also a member of both the Ministry of Defence (MoD) University Defence Research Centre in Signal Processing and the BBC Audio Research Partnership. His current research interests are in the areas of blind signal processing, machine audition (listening), audio-visual signal processing, sparse signal processing, machine learning and perception. His research is funded by the Engineering and Physical Sciences Research Council, Ministry of Defence, Defence Sciences and Technology Laboratory, Home Office, Royal Academy of Engineering and the University Research Support Fund.

• Section 1: Theory, Algorithms and ExtensionsQuantum independent component analysis and related statistical blind qubit uncoupling methods.- Blind source separation based on dictionary learning: a singularity-aware approach.- Performance study for complex independent component analysis.- Sub-band based- blind source separation and permutation alignment.- Frequency domain blind source separation based on independent vector analysis with a multivariate Gaussian source prior.- Sparse component analysis: a general framework for linear or nonlinear blind unmixing of signals or images.- Underdetermined audio source separation using Laplacian mixture modelling.- Itakura-Saito nonnegative matrix two-dimensional factorizations for blind single channel audio separation.- Source localisation and tracking: a maximum a posterior based approach.- Section 2: Applications Statistical analysis and evaluation of blind speech extraction algorithms.- Speech separation and extraction by combining super directive beam forming and blind source separation.- On the ideal ratio mask as the goal of computational auditory scene analysis.- Monaural speech enhancement based on multi-threshold masking.- REPET for background/foreground separation.- Non-negative matrix factorization sparse coding strategy for cochlear implants.- Exploratory analysis of brain with ICA.- Supervised normalisation of large-scale omic datasets using blind source separation.- FebICA: feedback independent component analysis for complex domain source separation of communication signals.- Semi-blind functional source separation algorithm from non-invasive electrophysiology to neuroimaging.