Determined Blind Source Separation Unifying Independent Vector Analysis and Nonnegative Matrix Factorization
Determined Blind Source Separation Unifying Independent Vector Analysis and Nonnegative Matrix Factorization
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DOI：
10.1109/taslp.2016.2577880
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发表时间：
2016-09-01
影响因子：
5.4
通讯作者：
Saruwatari, Hiroshi
中科院分区：
文献类型：
--
作者：
Kitamura, Daichi;Ono, Nobutaka;Saruwatari, Hiroshi
本文探讨了确定型盲源分离问题，并提出了一种将独立向量分析（IVA）与非负矩阵分解（NMF）相结合的新型有效方法。IVA是一种前沿技术，它利用混合信号中各源信号之间的统计独立性，并且已针对IVA提出了一种高效的优化方案。然而，由于IVA中的源模型基于球形多元分布，IVA无法利用特定的频谱结构，比如有调乐器声音的谐波结构。为解决这一问题，我们在IVA中引入NMF分解作为源模型，以捕捉频谱结构。所提方法的公式推导自传统的多通道NMF（MNMF），这揭示了MNMF与IVA之间的关系。所提方法可通过IVA和单通道NMF的更新规则进行优化。实验结果表明，与IVA和MNMF相比，所提方法在分离精度和收敛速度方面具有有效性。
This paper addresses the determined blind source separation problem and proposes a new effective method unifying independent vector analysis (IVA) and nonnegative matrix factorization (NMF). IVA is a state-of-the-art technique that utilizes the statistical independence between sources in a mixture signal, and an efficient optimization scheme has been proposed for IVA. However, since the source model in IVA is based on a spherical multivariate distribution, IVA cannot utilize specific spectral structures such as the harmonic structures of pitched instrumental sounds. To solve this problem, we introduce NMF decomposition as the source model in IVA to capture the spectral structures. The formulation of the proposed method is derived from conventional multichannel NMF(MNMF), which reveals the relationship between MNMF and IVA. The proposed method can be optimized by the update rules of IVA and single-channel NMF. Experimental results show the efficacy of the proposed method compared with IVA and MNMF in terms of separation accuracy and convergence speed.