Advances in Time-Frequency Analysis for Blind Source Separation: Challenges, Contributions, and Emerging Trends

Abstract

Blind source separation (BSS) is a critical task in untangling non-stationary signals without prior information. This paper extensively explores diverse time-frequency analysis (TFA) methods within BSS systems over the past decade. It underscores the pivotal role of TFA in dealing with non-stationary signals by characterizing their attributes across time and frequency domains. This approach provides a comprehensive understanding of signal dynamics that surpasses conventional techniques focusing solely on temporal or spectral domains. The paper delves into various TFA methods, investigating their influencing factors and aiding researchers in selecting relevant techniques aligned with their objectives. Furthermore, it comprehensively reviews contemporary research, categorizing BSS algorithms into three classes. The role of commonly used TFA methods in each class is systematically evaluated, identifying their strengths and limitations during different separation stages. The paper addresses challenges in implementing BSS algorithms, particularly in under-determined systems with fewer mixing channels than source signals. It highlights the central role of TFA in overcoming these challenges and enhancing separation outcomes.