Blind source separation problem algorithms for audio and biomedical signals

摘要

Blind Source Separation (BSS) is an inverse solution problem based on Second Order Statistic (SOS) and Higher Order Statistic (HOS), with significant applications in the area of acoustics, telecommunications, and biomedicine. In this work, one algorithm based on SOS called Second Order Blind Identification (SOBI), and two HOS algorithms, max-kurtosis, and fastICA was compared using mixed of known signals as sinusoidal, and audio waveforms in order to compare the performance of algorithms using one referenced metric between original and recovered signals, as well as five EEG nearby channels located in a specifical regions on the scalp. The results suggest that fastICA algorithm based on negentropy is performed better in most cases, however, in EEG signals, it is not possible to conclude the same due to the lack of ground truth, therefore, the choice of better ICA algorithm to separate EEG signals it is still an open problem in the biomedical applications.