A Survey of Artificial Intelligence Approaches in Blind Source Separation

In various signal processing applications, such as audio signal recovery, the extraction
of desired signals from a mixture of other signals is a crucial task. To achieve superior
performance and efficiency in separator systems, extensive research has been
conducted. Blind source separation emerges as a relevant technique to address the
challenge of separating and reconstructing unknown signals when only observations of
their mixtures are available to end-users. Blind source separation involves retrieving a
set of independent source signals mixed by an unknown and potentially destructive
combining system. Notably, the separation process in blind source separation
frameworks solely relies on observing the mixed sources without prior knowledge of
the mixing algorithm or the source signal characteristics. The significance of blind
source separation has garnered substantial attention, and its numerous applications
have been demonstrated, which serves as the primary motivation for conducting this
comprehensive study. This paper presents a systematic literature survey of blind
source separation, encompassing existing methods, approaches, and applications,
with a particular focus on artificial intelligence-based frameworks. Through a thorough
review and examination, this work sheds light on the diverse techniques utilized in
blind source separation and their performance in real-world scenarios. The study
identifies research gaps in the current literature, highlighting areas that warrant further
investigation and improvement. Moreover, potential avenues for future research are
outlined to contribute to the ongoing development of blind source separation
techniques.