Abstract

While Independent Subspace Analysis provides a means of blindly separating sound sources from a single channel signal, it does have a number of problems. In particular the amount of information required for separation of sources varies with the signal. This is as a result of the variance-based nature of Principal Component Analysis, which is used for dimensional reduction in the Independent Subspace Analysis algorithm. In an attempt to overcome this problem the use of a non-variance based dimensional reduction method, Locally Linear Embedding, is proposed. Locally Linear Embedding is a geometry based dimensional reduction technique. The use of this approach is demonstrated by its application to single channel source separation, and its merits discussed.