Wind noise short term power spectrum estimation using pitch adaptive inverse binary masks

This paper presents a method to enhance a speech signal disturbed by wind noise. The wind noise is generated by turbulences in an air stream close to the microphone which picks up the desired speech signal. As the majority of speech enhancement algorithms works in the frequency domain, the short term power spectrum (STPS) of the unwanted noise must be estimated to reduce the wind noise. Conventional algorithms for background noise estimation fail in the case of wind noise due to its non-stationary characteristics. Hence, it is necessary to use special methods for the estimation and reduction of wind noise. The proposed system exploits the spectral characteristics of speech and noise to estimate the wind noise STPS. The spectral power distribution of wind noise and the pitch frequency of speech are used to generate a binary mask for the noise STPS estimation. This method is dependent on a precise pitch estimation. To reduce estimation errors a robust pitch estimation method using knowledge from prior estimates is presented. An evaluation and comparison with other wind noise reduction techniques shows improved speech enhancement of the proposed method.