Using multiple frequency bins for stabilization of FD-ICA algorithms

Abstract

a b s t r a c tIn 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.

Blind Source Separation Using Mixtures of Alpha-Stable Distributions

“…It notably occurs when using ICA [5] and clustering-based methods [8,25]. A number of techniques have been proposed to tackle it, based on temporal activation patterns [25], steering vector models [8] or adjacent frequency bands similarity [29]. The selection and tuning of a specific permutation technique highly depends on the type of signal and mixing model considered, which is out of the scope of this study.…”

A Bayesian approach to convolutive nonnegative matrix factorization for blind speech dereverberation

“…This effort has already generated many advances in a wide variety of fields such as automatic speech recognition ( [1]), automatic translation systems ( [2]) and control of remote devices through voice ( [3]), to name only a few. A significant amount of work has been recently devoted to produce robustness in speech recognition ( [4]), resulting in several advances in the areas of speech enhancement ( [1], [5]), multiple sources separation ( [6], [7]), and particularly in dereverberation techniques ( [8]), which constitute the topic of this work.…”

On the use of convolutive nonnegative matrix factorization with mixed penalization for blind speech dereverberation

“…This effort has already generated many advances in a wide variety of fields such as automatic speech recognition ( [1]), automatic translation systems ( [2]) and control of remote devices trough voice ( [3]), to name only a few. A significant amount of work has been recently devoted to produce robustness in speech recognition ( [4]), resulting in several advances in the areas of speech enhancement ( [1], [5]), multiple sources separation ( [6], [7]), and particularly in dereverberation techniques ( [8]), which constitute the topic of this work.…”