Acceleration of rank-constrained spatial covariance matrix estimation for blind speech extraction
This work addresses computational efficiency for speech extraction in noisy environments, but it is incremental as it builds on existing rank-constrained models.
The paper tackled the problem of slow computation in rank-constrained spatial covariance matrix estimation for blind speech extraction by proposing accelerated update rules that eliminate matrix inversion and multiplication, achieving an 87 times faster calculation than the naive method.
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.