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EPSRC Research Network on Blind Source Separation and Independent Component Analysis (ICA Research Network) |
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ICArn 1-day Workshop on Applied BSS/ICAICA Research Network (ICArn) Workshop on Applied Blind Source Separation and Independent Component AnalysisUniversity of Southampton
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| 09:00 | Registration | SC |
| 09:30 | Welcome Christopher James, University of Southampton |
LT |
| 09:40 | ICA in Neuroinformatics |
LT |
| 10:40 | Coffee Break | SC |
| 11:10 | Control of coloration by cuttlefish |
LT |
| 12:10 | Lunch and Posters (see below) | SC |
| 14:00 | Blind audio source separation - a critical review Emmanuel Vincent, Queen Mary University of London [Slides: pdf] |
LT |
| 15:00 | Tea Break | SC |
| 15:30 | Panel Discussion |
LT |
| 16:30 | Closing remarks | LT |
| 17:00 | Workshop ends |
Key: SC - Staff Club; LT - Shackleton Building Lecture Theatre
The presentations will be given in the Shackleton Building Lecture Theatre, on the Highfield Campus of the University of Southampton.
When booking hotels, please advise them that you are visiting the University: many of the hotels offer special rates.
Due to the Southampton Boat Show, you may need to contact a few hotels, since some hotels near the City Centre may already be full.
Bayesian approaches to time-frequency based blind audio source separation
Cédric Févotte and Simon J. Godsill
Cambridge University Engineering Dept
Using Video to Solve the Cocktail Party Problem
A. Aubrey, Y. Hicks, J. Chambers, S. Sanei and D. Marshall
Cardiff University
Recent research at Edinburgh in BSS and ICA
James R. Hopgood and Bernie Mulgrew
University of Edinburgh
A maximum likelihood algorithm for slow feature analysis
Richard Turner and Maneesh Sahani
Gatsby Institute, UCL
Latent Process Decomposition of Microarray Data
Simon Rogers and Mark Girolami
University of Glasgow
Adaptive Image fusion using ICA bases
Nikolaos Mitianoudis and Tania Stathaki
Imperial College London
Speech Modelling with Application to Convolutive Blind Source Separation
Kostas Kokkinakis and Asoke K. Nandi
University of Liverpool
Blind OFDM Receivers based on Independent Component Analysis for Multiple-Input
Multiple-Output Systems
Luciano Sarperi, Xu Zhu and Asoke K. Nandi
University of Liverpool
Towards Agent-Based Independent Component Analysis
Roman Belavkin
Middlesex University
Tensorial extensions to ICA for multi-subject/session FMRI data
Christian Beckman
University of Oxford
ICA in a Computer Game
Colin Fyfe
University of Paisley
BSS on real data: tracking and implementation
Paul Baxter
Qinetiq
Speech and music applications of independent component analysis
Maria Jafari et al
Queen Mary University of London
Temporal ICA for automatic artefact removal from EEG
N. Nicolaou and S. J. Nasuto
University of Reading
Tracking Epileptiform Activity in the Multichannel Ictal EEG using Spatially
Constrained Independent Component Analysis
Christian Hesse, Christopher James
University of Southampton
Independent component analysis, a new framework for speech processing
of cochlear implant?
Guoping LI, Mark E Lutman
University of Southampton
Biomedical applications are arguably the most successful practical uses of Independent Component Analysis. During the presentation, I will pass in review early research carried out in the Neural Networks Research Centre. We will cover topics such as artifact removal in EEG, MEG and fMRI; the analysis of even related activity; the introduction of prior information to ICA, in search for more physiologically plausible components; the study of cortico-muscle interaction. The assessment of the reliability of the ICA decomposition will also be addressed, supported by a case study in fMRI.
Blind Audio Source Separation (BASS) aims to recover the waveform of each source within an audio signal containing several audio sources active simultaneously. This has many direct applications, including denoising for auditory protheses, post-production of raw musical recordings and rendering of stereo data on multichannel devices.
Early work on BASS includes Independent Component Analysis (ICA), which uses a generic statistical model to separate multi-channel signals with low reverberation, and Computational Auditory Scene Analysis (CASA), which exploits specific properties of audio sources such as harmonicity and timbre to separate single-channel signals. More recently, statistical models using time-frequency masking have been proposed for the separation of stereo signals. In addition, advanced statistical source models, such as factorial hidden Markov models and sparse nonnegative factorization of the power spectrogram, have also been applied to single- and multi-channel BASS.
This talk will provide a critical review of these established and recent methods as applied to real world BASS problems. The different approaches will be compared within a unified statistical framework and the special features of audio data will be pointed out. Finally a list of open problems will be proposed for further research and promising techniques such as probabilistic waveform modeling will be mentioned.
Ordinary behaviour requires the action of many muscles controlled by the nervous system. One can conceive behaviours as being produced by a number of discrete neural centres, whose outputs can in some cases be mixed. In visual communication well-known examples are found in the expression of emotion by man and animals, where a range of expressions are produced by mixing a few basic states - fear, aggression, pleasure and disgust. We are using ICA to investigate the related problem of how the common cuttlefish co-ordinates the expression of some 50 separate visual and postural features to produce a highly flexible system for camouflage and communication. In principle ICA is well-suited to this problem as the patterns are too complex to analyse visually but combine approximately linearly to determine the visual appearance of the animals body.
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This page updated 09-Jan-2007