The subject of this project is Blind Source Separation (BSS) and Independent Component Analysis (ICA).
The main objectives of the project are to achieve significant advances in:
1. Development of theory and algorithms for linear ICA (in particular instantaneous and convolutive ICA, noisy and adaptive ICA), for non-linear ICA, and for separation of non-independent signals.
2. Two main application areas: biomedical signals and acoustic mixtures
DESCRIPTION OF WORK
The main item of the work to be performed are:
1. Theory and algorithms
- Analysis of open issues in linear ICA.
- Development of better techniques for noisy and/or adaptive ICA
- Development of theory and algorithms for nonlinear ICA.
- Development of theory and algorithms for separation of non-independent signals.
In all these aspects, theoretical issues will be addressed, side by side with the development of algorithms.
- Applications of these techniques and algorithms to biomedical signals (especially MEG and MNG) for artefact extraction and elimination and for a better interpretation of these signals.
- Application for decomposing evoked fields in biomedical signals, enabling direct access to the underlying brain functioning.
- Application to the separation of acoustic mixtures, first in well controlled situations and progressively proceeding to less controlled ones (actual persons as speaker, noise, moving sources and microphones).
- Application of the separation of acoustic mixtures to the development of a new generation of hearing aids with a much better handling of noisy environments, taking into account the technological constraints for implementing the processing within a wearable device.
Funding SchemeCSC - Cost-sharing contracts
L8S 4L8 Hamilton