| Paper ID | MLSP-35.4 |
| Paper Title |
Blind Extraction of Moving Sources via Independent Component and Vector Analysis: Examples |
| Authors |
Nesrine Amor, Jaroslav Cmejla, Technical Unversity of Liberec, Czechia; Vaclav Kautsky, Czech Technical University in Prague, Czechia; Zbynek Koldovsky, Tomas Kounovsky, Technical University of Liberec, Czechia |
| Session | MLSP-35: Independent Component Analysis |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-ICA] Independent component analysis |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
This paper is devoted to the recently proposed mixing model with constant separating vector (CSV) for Blind Source Extraction of moving sources using the FastDIVA algorithm, which is an extension of the famous FastICA and FastIVA for static mixtures. The benefits due to the CSV model and FastDIVA are demonstrated in three new applications. First, the extraction of a moving speaker in a noisy reverberant environment using a dense array of 48 MEMS microphones is considered. Second, a case study on the blind extraction of moving brain activity from visually evoked potentials in electroencephalogram is reported. Third, a simulation of block-by-block online extraction of a moving source is demonstrated. In these examples, the CSV and FastDIVA show their new potential and good performance in handling the blind moving source extraction problem. |
