A survey of artificial intelligence approaches in blind source separation

Abstract

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.