Using multiple frequency bins for stabilization of FD ICA algorithms
In the frequency domain independent component analysis approaches for audio sources separation, the convolutive mixing problem is replaced by the solution of several instantaneous mixing problems, one for each frequency bin of the short time Fourier transform. This methodology yields good results but requires the solution of the permutation ambiguity. Moreover, the performance of the separation algorithms for each bin is not guaranteed to be equivalent, thus some bins can have worse results than others. In this paper a technique based on data from multiple bins is proposed to address these issues. The use of multiple bin information produces a coupling of the separation, resulting in more stable separation matrices and reducing the occurrence of permutations, but increasing the computational cost. This can be mitigated by a sub sampling of the multiple bins information. The results show that both approaches are beneficial for the frequency domain ICA approach, producing better separation in terms of objective quality measures. HighlightsA method for stabilization of FD ICA using lateral bins information is proposed.The method produces a robust estimation of separation matrices for all bins.The permutation rate is also reduced which improves the separation quality.Using subsampling the introduced computational cost overhead can be reduced.The trade off between quality improvement and overhead can be controlled.
Signal Process.