Independent Vector

Independent Vector Analysis (IVA) is a signal processing technique extending Independent Component Analysis (ICA) to separate multiple sources from their mixtures, particularly useful in scenarios with multiple channels or frequencies. Current research focuses on improving IVA's robustness and efficiency, particularly addressing challenges like block permutation problems and noisy environments, often employing algorithms like FastICA/FastIVA and incorporating techniques such as subband splitting and low-rank matrix analysis (ILRMA). These advancements have significant implications for applications such as blind source separation in audio processing, speech recognition, and enhancing the security of deep neural networks by detecting malicious backdoors.