Acceleration of rank-constrained spatial covariance matrix estimation for blind speech extraction
Abstract
In this paper, we propose new accelerated update rules for rank-constrained spatial covariance model estimation, which efficiently extracts a directional target source in diffuse background noise.The naive updat e rule requires heavy computation such as matrix inversion or matrix multiplication. We resolve this problem by expanding matrix inversion to reduce computational complexity; in the parameter update step, we need neither matrix inversion nor multiplication. In an experiment, we show that the proposed accelerated update rule achieves 87 times faster calculation than the naive one.
Cite
@article{arxiv.1908.01964,
title = {Acceleration of rank-constrained spatial covariance matrix estimation for blind speech extraction},
author = {Yuki Kubo and Norihiro Takamune and Daichi Kitamura and Hiroshi Saruwatari},
journal= {arXiv preprint arXiv:1908.01964},
year = {2019}
}
Comments
7 pages, 3 figures, To appear in the Proceedings of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2019 (APSIPA 2019)