Abstract
Machine-based multi-channel source separation in real life situations is a challenging problem, and has a wide range of applications, from medical to military. With the increase in computational power available to everyday devices, source separation in real-time has become more feasible, contributing to the boost in the research in this field in the recent past. Algorithms for source separation are based on specific assumptions regarding the source and signal model – which depends upon the application. In this chapter, the specific application considered is that of a target speaker enhancement in the presence of competing speakers and background noise. It is the aim of this contribution to present not only an exhaustive overview of state-of-the-art separation algorithms and the specific models they are based upon, but also to highlight the relations between these algorithms, where possible. Given this wide scope of the chapter, we expect it will benefit both, the student beginning his studies in the field of machine audition, and those already working in a related field and wishing to obtain an overview or insights into the field of multi-channel source separation.
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