The Blind Source Separation (BSS) problem was first introduced (Herault et al., 1985; Jutten & Herault, 1988) in the context of biological problems (Ans et al., 1983; Herault & Ans, 1984) with the aim of being able to separate a set of signals generated by the central nervous system. A few years later, several methods based on BSS were applied to other fields of industry and research (Deville, 1999). The BSS problem arises from the need to recover the original sources from a blindly mixture. This extraction is characterised as a blind process because the lack of information about the following topics: the characterisation of the sources, the number of sources present at the time of the mixture, and the way that this mixture is performed. Although this kind of information is unknown, the problem described can be solved if the input signals to the mixture process are statistically independent. Related literature provides several methods, most of which have been classified according to the context in which the mixture is performed: linear mixture model, convolutive mixture model, and non-linear mixture model. The first part of this chapter is devoted to describe the most relevant existing works in applying these methods to the audio field. Many of the real problems, however, do not support this simplification, so this part stresses the need for full characterisation of the problem, mainly about the mixing process and the nature of the sources involved.