Binaural Wind-Noise Tracking with Steering Preset

Abstract

Optimal performance of many speech enhancement methods is bound to an accurate noise power-spectral density (PSD) estimation. While for stationary noises, such as the white Gaussian or car noise, several approaches have proven themselves to perform sufficiently good, non-stationary noise types like the wind noise are more challenging. In the binaural setting and in multichannel systems, the speech-blocking method is essential to recent developments for non-stationary noise estimation. It critically requires information of the acoustic channel transfer function from source to listener. In this paper, we propose such noise-subspace approach for wind-noise PSD estimation, which relies on data-driven blind channel identification in speech presence and on a-priori acoustic channel information (i.e., the steering preset) in speech pause, where the smooth transition of both is controlled by a-priori SNR. The algorithm is designed for entire online operation based on the current noisy frame input. It improves on straightforward recursive subspace analysis and on established single-channel estimation in the wind-noise scenario, while dealing well with speech presence or babble noise too.