English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we address the multichannel blind source extraction (BSE) of asingle source in diffuse noise environments. To solve this problem even fasterthan by fast multichannel nonnegative matrix factorization (FastMNMF) and itsvariant, we propose a BSE method called NoisyILRMA, which is a modification ofindependent low-rank matrix analysis (ILRMA) to account for diffuse noise.NoisyILRMA can achieve considerably fast BSE by incorporating an algorithmdeveloped for independent vector extraction. In addition, to improve the BSEperformance of NoisyILRMA, we propose a mechanism to switch the source modelwith ILRMA-like nonnegative matrix factorization to a more expressive sourcemodel during optimization. In the experiment, we show that NoisyILRMA runsfaster than a FastMNMF algorithm while maintaining the BSE performance. We alsoconfirm that the switching mechanism improves the BSE performance ofNoisyILRMA.

NoisyILRMA: Diffuse-Noise-Aware Independent Low-Rank Matrix Analysis for Fast Blind Source Extraction