Blind separation of time-varying signal mixtures using sparseness and Zadeh's transform

We consider the general problem of blindly separating time-varying mixtures. Physical phenomena, such as varying attenuation and the doppler effect, can be represented as special cases of a time-varying mixing model. This model can be considered as a linear mixing of time-varying attenuated-and-delayed versions of fixed channel distortions. In this special case, we use Zadeh's transform to project the signals to the time-frequency domain. In this domain, sparse source distribution highlights geometric properties of the mixing coefficients. These coefficients can be used in turn, for inverting the mixing system, and thereby, recover the time-varying filtered versions of the original sources.