Independent low-rank matrix analysis (ILRMA) is the state-of-the-art algorithm for blind source separation (BSS) in the determined situation (the number of microphones is greater than or equal to that of source signals). ILRMA achieves a great separation performance by modeling the power spectrograms of the source signals via the nonnegative matrix factorization (NMF). Such a highly developed source model can solve the permutation problem of the frequency-domain BSS to a large extent, which is the reason for the excellence of ILRMA. In this paper, we further improve the separation performance of ILRMA by additionally considering the general structure of spectrograms, which is called consistency, and hence we call the proposed method Consistent ILRMA. Since a spectrogram is calculated by an overlapping window (and a window function induces spectral smearing called main- and side-lobes), the time-frequency bins depend on each other. In other words, the time-frequency components are related to each other via the uncertainty principle. Such co-occurrence among the spectral components can function as an assistant for solving the permutation problem, which has been demonstrated by a recent study. On the basis of these facts, we propose an algorithm for realizing Consistent ILRMA by slightly modifying the original algorithm. Its performance was extensively evaluated through experiments performed with various window lengths and shift lengths. The results indicated several tendencies of the original and proposed ILRMA that include some topics not fully discussed in the literature. For example, the proposed Consistent ILRMA tends to outperform the original ILRMA when the window length is sufficiently long compared to the reverberation time of the mixing system.

中文翻译：

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用于确定盲源分离的一致独立低秩矩阵分析

独立低秩矩阵分析（ILRMA）是在确定情况下（麦克风数量大于或等于源信号数量）盲源分离（BSS）的最新算法。ILRMA 通过非负矩阵分解 (NMF) 对源信号的功率谱图进行建模，从而实现了出色的分离性能。如此高度发达的源模型可以在很大程度上解决频域BSS的置换问题，这也是ILRMA优秀的原因。在本文中，我们通过额外考虑频谱图的一般结构（称为一致性）来进一步提高 ILRMA 的分离性能，因此我们将所提出的方法称为 Consistent ILRMA。由于频谱图是通过重叠窗口计算的（并且窗口函数会引起称为主瓣和旁瓣的频谱拖尾现象），因此时间频率区间相互依赖。换句话说，时频分量通过不确定原理相互关联。光谱分量之间的这种共现可以作为解决置换问题的助手，最近的一项研究已经证明了这一点。在这些事实的基础上，我们提出了一种通过对原始算法稍加修改来实现一致 ILRMA 的算法。通过使用各种窗口长度和移位长度进行的实验，对其性能进行了广泛的评估。结果表明原始和提议的 ILRMA 的几种趋势，其中包括一些文献中未充分讨论的主题。例如，