抽象的

DETECTION OF UNDERDETERMINED SOURCE SEPARATION USING GASSIAN PROCESSES

JAMRUD KHAN , Dr. S.MADHAVA KUMAR, Dr. S. BHARGAVI

In this paper, we have stated the linear underdetermined, instantaneous, convolutive and multiple-output source separation problems in terms of Gaussian processes regression and. The advantages of setting out the source separation problem in terms of GP are numerous. First, there is neither notational burden nor any conceptual issue raised when using input spaces X different from R or Z, thus enabling a vast range of source separation problems to be handled within the same framework. Multi-dimensional signal separation may include audio, image or video sensor arrays as well as geostatistics. Secondly, GP source separation can perfectly be used for the separation of non locallystationary signals. Of course, some important simplifications of the computations as are lost when using non-stationary covariance functions. Thirdly, it provides a coherent probabilistic way to take many sorts of relevant prior information into account.