On the effectiveness of PARAFAC-based estimation for blind speech separation

This work establishes the effectiveness of parallel factor (PARAFAC) analysis in blind speech separation (BSS) problems. The BSS problem is formulated as a conjugate-symmetric PARAFAC model that is fitted optimally, using an efficient alternating least-squares algorithm that converges monotonically. The identifiability properties of the model are also presented, revealing the much broader identifiability potential of joint-diagonalization- based BSS methods. In order to focus on estimation performance, perfect resolution of the permutation ambiguity is assumed. Simulations under varying reverberation conditions and comparison with previous estimation methods that are widely used in BSS problems demonstrate significant performance gains. Signal-to- interference (SIR) ratio improvement of over 27 dB is achieved using PARAFAC. Average SIR gains of 2.5 and 6.3 dB are achieved compared to state-of-the-art FastICA[2] and FDSOS (Parra´s)[5] estimation algorithms, respectively.