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.

独立的低秩矩阵分析（ILRMA）是在确定的情况下（麦克风的数量大于或等于源信号的数量）的盲源分离（BSS）的最新算法。ILRMA通过通过非负矩阵分解（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。这种高度发展的源模型可以在很大程度上解决频域BSS的置换问题，这是ILRMA出色的原因。在本文中，我们通过进一步考虑频谱图的一般结构（称为一致性）进一步提高了ILRMA的分离性能，因此，我们将所提出的方法称为“一致性ILRMA”。。由于频谱图是由重叠的窗口计算的（窗口函数会引起频谱模糊，称为主瓣和旁瓣），因此时频点彼此依赖。换句话说，时频分量通过不确定性原理相互关联。频谱分量之间的这种共现可以作为解决置换问题的辅助工具，最近的研究已经证明了这一点。基于这些事实，我们提出了一种通过稍微修改原始算法来实现一致性ILRMA的算法。通过对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。结果表明了原始和提议的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，