Consistent Independent Low-Rank Matrix Analysis for Determined Blind Source Separation
Daichi Kitamura, Kohei Yatabe

TL;DR
This paper enhances the ILRMA algorithm for blind source separation by incorporating spectrogram consistency, leading to improved separation performance especially with longer window lengths.
Contribution
The paper introduces Consistent ILRMA, which integrates spectrogram consistency into ILRMA to better utilize spectral dependencies for improved source separation.
Findings
Consistent ILRMA outperforms original ILRMA with longer window lengths.
Spectrogram consistency helps in solving the permutation problem.
Performance varies with window and shift lengths.
Abstract
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),…
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