Multi-channel Wind Noise Reduction Using the Corcos Model

Outdoor recordings of speech are often corrupted by wind noise, which is difficult to reduce due to its high non-stationarity. In this work, a multi-channel wind noise reduction method is presented, based on a joint estimation of the speech and wind noise power spectral densities. In contrast to existing methods that assume uncorrelated wind noise, the estimation phase is performed exploiting the spatial characteristics of wind noise measured by closely-spaced microphones. Here the characteristics are approximated by a fluid-dynamics model, termed the Corcos model. An additional contribution is the employment of a frequency dependent trade-off parameter in the reduction phase, which depends on the ratio of the difference-signal power to the sum-signal power of a sub-set of two microphones. In particular, the trade-off parameter of the parametric multi-channel Wiener filter is used to control the trade-off between noise reduction and speech distortion. The proposed method can be used also to reduce spatially uncorrelated wind noise. An evaluation in terms of speech quality and signal-to-noise ratio improvements is carried out to compare the proposed method to an existing multichannel wind noise reduction method, using recorded and simulated wind noise samples.