(Національний технічний університет "Харківський політехнічний інститут", 2024) Monastyrskyi, Mykyta
Blind signal separation is the task of separating the given mixture signal into two or more corresponding sources. It finds an application in many fields of human activity such as medicine, telecommunications, art and many more and is a crucial task in signal processing. However, the task itself appears to be quite challenging due to its ill-posed nature. Despite that many modern machine learning-based approaches achieve the state-of-the-art results in different blind source separation tasks (e. g. audio or music source separation) however these methods can suffer from unwanted artifacts in the source signals estimates. This paper presents an overview of the methods for blind source separation covering methods from traditional statistical ones to modern machine learning-based approaches and applications of the results of blind source separation task. Moreover, we discuss some potential areas of research in the field of blind source separation to facilitate further research and develop powerful solutions for this task.
