Robust wind noise detection

Abstract

Wind noise can be a major problem with audio devices such as hearing aids, cochlear implants, phones and headsets. Previous wind-noise detection algorithms generally assume that large level and/or phase differences between two microphones indicate wind noise, while small differences indicate its absence. However, differences may exist without wind noise due to unmatched microphones, acoustic reflections, or the phase shift caused by the microphone spacing. This paper shows that previous algorithms do not always correctly differentiate between wind and non-wind causes of microphone signal differences, which could lead to the inappropriate engagement of wind-noise reduction processing. A novel algorithm is presented, which performs an efficient statistical analysis of the microphone signals that is substantially more robust against non-wind causes differences, and hence false wind-noise detection, in an exemplary hearing-aid application.

Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array

“…Wind noise is generated by the pressure variations from flow turbulence deflect the microphone diaphragm [10]. Therefore, we presume that wind noise is the laminar model for simplicity.…”

“…However, they pay little attention to wind noise, a particular type of noise in wild reconnaissance, which is hard to handle in the invasion detection application. Wind noise is generated by the pressure variations from flow turbulence deflect the microphone diaphragm [10]. Therefore, the wind turbulence between microphones is comparatively uncorrelated, which can be used to distinguish the sound waves from the wind noise [11,12].…”

“…A min peak correlation coefficient measure was designed in [12] for wind noise analysis using large aperture array with radius of one meter. Yet the low correlation is not a reliable feature for non-stationary wind noise detection with SAMA [10]. Therefore, the ${\chi }^2$ algorithm to robust wind noise detection was introduced in [10] for audio devices such as hearing aids, cochlear implants, phones and headsets.…”

“…Yet the low correlation is not a reliable feature for non-stationary wind noise detection with SAMA [10]. Therefore, the ${\chi }^2$ algorithm to robust wind noise detection was introduced in [10] for audio devices such as hearing aids, cochlear implants, phones and headsets. However, few researches exploiting the delay time between SAMA sensors to detect the wind noise in surveillance sensor except for calculating the velocity of sound [13] and wind [14].…”

On the Difference-to-sum Power Ratio of Speech and Wind Noise Based on the Corcos Model

“…State-of-the-art device architectures (e.g., smartphones or hearing aids) are often equipped with more than one microphone and therefore a multi-channel processing can be performed, as in [7][8][9][10]. In particular, existing multi-channel algorithms are based on either time or frequency domain-based detection approaches [11,12].…”