Frame-Based Space-Time Covariance Matrix Estimation for Polynomial Eigenvalue Decomposition-Based Speech Enhancement

Authors: Emilie d’Olne, Vincent W. Neo, Patrick A. Naylor. Demo | Poster | Paper

As the number of connected devices equipped with multiple microphones increases, scientific interest in distributed microphone array processing grows. Current beamforming methods heavily rely on estimating quantities related to array geometry, which is extremely challenging in real, non-stationary environments. Recent work on polynomial eigenvalue decomposition (PEVD) has shown promising results for speech enhancement in singular arrays without requiring the estimation of any array-related parameter [1]. This work extends these results to the realm of distributed microphone arrays, and further presents a novel framework for speech enhancement in distributed microphone arrays using PEVD. The proposed approach is shown to almost always outperform optimum beamformers located at arrays closest to the desired speaker. Moreover, the proposed approach exhibits very strong robustness to steering vector errors.

References

[1]  V. W. Neo, C. Evers, and P. A. Naylor, “Enhancement of noisy reverberant speech using polynomial matrix eigenvalue decomposition,” IEEE/ACM Trans. Audio, Speech, Language Process., vol. 29, pp. 3255– 3266, Oct. 2021.