Blind Source Separation

Blind source separation (BSS) aims to recover individual source signals from their mixed observations, without prior knowledge of the sources or mixing process. Current research emphasizes developing efficient algorithms, including Independent Component Analysis (ICA), Independent Vector Analysis (IVA), and deep learning-based approaches like autoencoders, often incorporating techniques like compressed sensing for improved efficiency and handling of underdetermined problems. These advancements are crucial for various applications, such as speech enhancement, biomedical signal processing (e.g., fetal ECG extraction), and improving the privacy of federated learning systems by mitigating gradient inversion attacks. The field is actively exploring improved model architectures and optimization strategies to enhance separation accuracy and reduce computational complexity, particularly for real-time applications.