Signal-adaptive robust time-varying Wiener filters: best subspace selection and statistical analysis

摘要

We propose a signal-adaptive robust time-varying Wiener filter for nonstationary signal estimation/enhancement. This filter uses projections onto local cosine subspaces and a novel "best subspace" algorithm. It allows efficient online operation including stable online estimation of design parameters. A statistical analysis is provided, and a speech enhancement example is considered.