A contextual blind separation of delayed and convolved sources

We present a new method to tackle the problem of separating mixtures of real sources which have been convolved and time-delayed under real world conditions. To this end, we learn two sets of parameters to unmix the mixtures and to estimate the true density function. The solutions are discussed for feedback and feedforward architectures. Since the quality of separation depends on the modeling of the underlying density we propose di erent methods to closer approximate the density function using some context. The proposed density estimation achieves separation of a wider class of sources. Furthermore, we employ the FIR polynomial matrix techniques in the frequency domain to invert a true-phase mixing system. The signi cance of the new method is demonstrated with the successful separation of two speakers and separation of music and speech recorded with two microphones in a reverberating room.