Simulating Wind Noise with Airflow Speed-Dependent Characteristics

Generating noise samples is crucial in developing and testing noise reduction algorithms or training deep learning models. This work proposes a wind noise generation model with airflow speed-dependent features. A linear predictive analysis of wind noise measured in a wind tunnel at different flow velocities was carried out. This analysis showed that temporal and spectral features depend on the flow speed. The prediction residual’s statistics and the filter coefficients are first extracted and then modeled based on the flow speed. The obtained models are then combined to synthetically generate wind noise given a time-varying flow speed profile as input, in contrast to an existing framework where temporal and spectral features were assumed speed-invariant. A subjective evaluation is carried out to assess the perceptual authenticity of the generated noise compared to the existing method.