Accelerating online algorithm using geometrically constrained independent vector analysis with iterative source steering
Kana Goto, Tetsuya Ueda, Li Li, Takeshi Yamada, Shoji Makino · 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · 2022
In this paper, we derive an alternative online algorithm for geometrically constrained independent vector analysis (GC-IVA) based on iterative source steering (ISS) to tackle real-time directional speech enhancement. The proposed algorithm fully exploits the advantages of the auxiliary function approach, i.e., fast convergence and no stepsize tuning, and ISS, i.e., low computational complexity and numerical stability, making it highly suitable for practical use. In addition, we investigate the performance impact of using estimated spatial information, which is assumed to be known as prior information in GC-IVA. Specifically, we evaluate the proposed algorithm with geometric constraints defined using directions of arrival (DOAs) estimated by the multiple signal classification (MUSIC) method. Experimental results revealed that the proposed online algorithm could work in real-time and achieve comparable speech enhancement performance with the conventional method called online GC-AuxIVA-VCD while significantly reducing execution times in the situation where a fixed target was interfered with by a moving interference.