Abstract
In this paper, we consider the problem of convolutive blind source separation in frequency domain and introduce a solution to the problem in an independent vector analysis (IVA) framework. IVA utilizes both the statistical independence of different sources in each frequency bin and the statistical dependence of the same source in different frequency bins. However, most of previous works impose orthogonality constraint on the rows of each separation matrix which may undermine the separation performance. In this work, we propose a nonorthogonal IVA algorithm based on decoupled relative Newton method. This proposed algorithm updates the separation matrices row by row, and unlike deflation separation algorithm, there is no separation error accumulation arising. Simulation results are provided to show the superior convergence behavior and separation performance of the proposed algorithm.
Highlights
Blind source separation (BSS) has been widely researched over the last decades since it is able to estimate the source signals from their mixtures without knowing both the mixing process and the sources, such as crosstalk separation in telecommunications [1], speech enhancement [2], and biomedical signal processing [3]
We propose a nonorthogonal independent vector analysis (IVA) algorithm based on decoupled relative Newton method
Various approaches have been proposed to deal with this case and they basically fall into two categories: time domain [8, 9] and frequency domain [10,11,12] algorithms
Summary
Blind source separation (BSS) has been widely researched over the last decades since it is able to estimate the source signals from their mixtures (observed sensor signals) without knowing both the mixing process and the sources, such as crosstalk separation in telecommunications [1], speech enhancement [2], and biomedical signal processing [3]. Independent vector analysis (IVA), an extension of ICA, can solve the frequency domain BSS problem efficiently and normally requires no bin-wise permutation correction post processing [10,11,12]. IVA algorithms extend the univariate function of ICA algorithms to multivariate function as the score function In this way, the separation for different frequency bin data is no longer separate but joint, and the permutation problem is mitigated by exploiting the dependences of frequency bins. Various algorithms employ different multivariate prior distributions to preserve the interfrequency dependences for individual sources and corresponding nonlinear score functions are derived Most of these algorithms impose orthogonality constraint on the rows of each separation matrix which may undermine the separation performance. We propose a nonorthogonal IVA algorithm based on decoupled relative Newton method.
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