Noise reduction in B-format recordings is particularly challenging as it concurrently requires to suppress undesired signals and preserve the spatial properties of the acoustic environment. In particular, wind noise poses an undesirable acoustic condition outdoors. In this work, methods to reduce wind noise while limiting the spatial distortions of the original signal are proposed based on recent works of the present authors. The main contributions are the derivation of the mentioned methods for B-format signals and the usage of the dipole-to-omnidirectional power ratio to control the trade-off between the desired-signal distortion and the noise reduction. The proposed methods are evaluated using B-format wind-noise recordings.

A deep neural network (DNN) based approach for estimating the speed of airflows using closely-spaced microphones is proposed. The spatial characteristics of wind noise measured with a smallaperture array are exploited, i.e., the low-frequency spatial coherence of wind noise signals is used as an input feature. The output is an estimate of the wind speed averaged over a specific time interval. The DNN is trained using synthetic wind noise, which overcomes the time-consuming data collection and allows to isolate wind noise from different acoustic sources. The dataset used for testing comprises wind noise measured outdoors with a circular linear array and a ground truth obtained using an ultrasonic anemometer. The obtained model is applied to generated and measured wind noise. The performance of the proposed method is assessed across a wide range of wind speeds and directions, using different time resolutions.

Outdoor recording is particularly challenging in the presence of wind, which induces highly non-stationary noise in the microphone signals. To enhance a desired signal, e.g., speech, a dedicated noise reduction processing is required. The reduction is usually performed by estimating an unknown set of parameters, e.g., the noise and the speech power spectral densities (PSDs). In contrast to the commonly used assumption of uncorrelated wind noise in multi-channel recordings, we assume the spatial correlation of wind noise contributions to be non-zero at low frequencies when closely-spaced microphones are employed. In our earlier work, we assumed that the spatial coherence was known, i.e., given by a model which depends on the free-field speed and direction of the air stream. As these quantities are unknown in practice, we propose in this work a method to recursively estimate the spatial coherence matrix based on the microphone observations. In addition, we prove the equivalence of two recently developed noise PSD estimation methods when uncorrelated wind noise is assumed, and we propose an approximation of both estimators which is independent of the propagation vector of the speech source at sufficiently low frequency and for a small inter-microphone distance. An evaluation in terms of improvements in speech quality, signal-to-noise ratio and intelligibility is carried out using both simulated and measured wind noise samples.

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.