This paper proposes a stereo wind-noise suppressor with frequency-domain noise averaging. A directional gain for diffuse wind noise is estimated frame by frame using a null beamformer based on interchannel phase difference which blocks the target signal. The wind-noise gain estimate is commonly multiplied by the input noisy signal to generate channel dependent wind noise estimates in order to cope with interchannel wind-noise imbalance. Interchannel phase agreement by target signal dominance or incidentally equal wind-noise phase, which leads to underestimation, is offset by averaging channel dependent wind-noise estimates along frequency. Evaluation results show that the mean PESQ score by the proposed wind-noise suppressor reaches 2.1 which is 0.2 higher than that by the wind-noise suppressor without averaging and 0.3 higher than that by a conventional monaural-noise suppressor with a statistically significant difference.

We propose a new algorithm for the blind source separation (BSS) approach in which independent component analysis (ICA) and frequency subband beamforming interpolation are combined. The slow convergence of the optimization of the separation filters is a problem in ICA. Our approach to resolving this problem is based on the relationship between ICA and null beamforming (NBF). The proposed method consists of the following three parts: (I) a frequency subband selector part for learning ICA, (II) a frequency domain ICA part with direction-of-arrivals (DOA) estimation of sound sources, and (III) an interpolation part in which null beamforming constructed with the estimated DOA is used. The results of the signal separation experiments under a reverberant condition reveal that the convergence speed is superior to that of the conventional ICA-based BSS methods.