Independent Low-Rank Matrix Analysis Based on Time-Variant Sub-Gaussian Source Model
Independent Low-Rank Matrix Analysis Based on Time-Variant Sub-Gaussian Source Model
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DOI：
10.23919/apsipa.2018.8659577
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发表时间：
2018-08
期刊：
影响因子：
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通讯作者：
Shinichi Mogami;Norihiro Takamune;Daichi Kitamura;H. Saruwatari;Yu Takahashi;Kazunobu Kondo;Hiroaki Nakajima;Nobutaka Ono
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文献类型：
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作者：
Shinichi Mogami;Norihiro Takamune;Daichi Kitamura;H. Saruwatari;Yu Takahashi;Kazunobu Kondo;Hiroaki Nakajima;Nobutaka Ono
独立低秩矩阵分析（ILRMA）是一种快速、稳定的盲音频源分离方法。传统的ILRMA假设时变（超）高斯源模型，其只能表示遵循超高斯分布的信号。在本文中，我们专注于基于广义高斯分布的ILRMA（GGD-ILRMA），并提出了一种新的类型的GGD-ILRMA，采用时变亚高斯分布的源模型。通过对均匀源模型采用一种新的更新方法--广义迭代投影，我们得到了一种保证收敛的空间参数分离更新规则。在实验评估中，我们展示了所提出的方法的多功能性，即，所提出的时变亚高斯源模型可以应用于各种类型的源信号。
Independent low-rank matrix analysis (ILRMA) is a fast and stable method for blind audio source separation. Conventional ILRMAs assume time-variant (super-)Gaussian source models, which can only represent signals that follow a super-Gaussian distribution. In this paper, we focus on ILRMA based on a generalized Gaussian distribution (GGD-ILRMA) and propose a new type of GGD-ILRMA that adopts a time-variant sub-Gaussian distribution for the source model. By using a new update scheme called generalized iterative projection for homogeneous source models, we obtain a convergence-guaranteed update rule for demixing spatial parameters. In the experimental evaluation, we show the versatility of the proposed method, i.e., the proposed time-variant sub-Gaussian source model can be applied to various types of source signal.