Efficient Full-Rank Spatial Covariance Estimation Using Independent Low-Rank Matrix Analysis for Blind Source Separation
This work addresses a specific challenge in audio signal processing for applications like speech enhancement, though it appears incremental as it builds on existing ILRMA methods.
The paper tackles the problem of separating directional sources from diffuse background noise in blind source separation by proposing a new algorithm that efficiently estimates full-rank spatial covariance using independent low-rank matrix analysis, showing improved computational cost and separation performance in experiments.
In this paper, we propose a new algorithm that efficiently separates a directional source and diffuse background noise based on independent low-rank matrix analysis (ILRMA). ILRMA is one of the state-of-the-art techniques of blind source separation (BSS) and is based on a rank-1 spatial model. Although such a model does not hold for diffuse noise, ILRMA can accurately estimate the spatial parameters of the directional source. Motivated by this fact, we utilize these estimates to restore the lost spatial basis of diffuse noise, which can be considered as an efficient full-rank spatial covariance estimation. BSS experiments show the efficacy of the proposed method in terms of the computational cost and separation performance.