Data-Driven Wind Speed Estimation Using Multiple Microphones

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 small-aperture 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.