Simulating Multi-channel Wind Noise Based on the Corcos Model

TL;DR

This paper introduces a new multi-channel wind noise generator based on the Corcos fluid dynamics model, enabling realistic synthetic wind noise signals with controllable spatial coherence for improved noise reduction testing.

Contribution

It extends an existing single-channel generator to produce multiple uncorrelated signals with desired coherence, validated through experiments matching real wind noise characteristics.

Findings

Synthetic signals match real wind noise in temporal, spectral, and spatial features.

The generator efficiently produces multiple uncorrelated signals with specified coherence.

Overcomes data collection challenges for wind noise in noise reduction research.

Abstract

A novel multi-channel artificial wind noise generator based on a fluid dynamics model, namely the Corcos model, is proposed. In particular, the model is used to approximate the complex coherence function of wind noise signals measured with closely-spaced microphones in the free-field and for time-invariant wind stream direction and speed. Preliminary experiments focus on a spatial analysis of recorded wind noise signals and the validation of the Corcos model for diverse measurement set-ups. Subsequently, the Corcos model is used to synthetically generate wind noise signals exhibiting the desired complex coherence. The multi-channel generator is designed extending an existing single-channel generator to create N mutually uncorrelated signals, while the predefined complex coherence function is obtained exploiting an algorithm developed to generate multi-channel non-stationary noise signals under a complex coherence constraint. Temporal, spectral and spatial characteristics of synthetic signals match with those observed in measured wind noise. The artificial generation overcomes the time-consuming challenge of collecting pure wind noise samples for noise reduction evaluations and provides flexibility in the number of generated signals used in the simulations.