Semi Blind Source Separation

Semi-blind source separation (SBSS) aims to recover individual signals from a mixture where some information about the mixing process is known, unlike fully blind methods. Current research focuses on improving computational efficiency, particularly for nonlinear applications like acoustic echo cancellation, and enhancing accuracy through learned constraints or unrolled optimization algorithms like variations of PALM and independent vector analysis (IVA). These advancements are significant for various fields, including astrophysics, remote sensing, and audio processing, by enabling more accurate and efficient signal disentanglement from complex mixtures.