Dual-channel noise reduction based on a mixture of circular-symmetric complex Gaussians on unit hypersphere

In this paper a model-based dual-channel noise reduction approach is presented which is an alternative to conventional noise reduction algorithms essentially due to its independence of the noise power spectral density estimation and of any prior knowledge about the spatial noise field characteristics. We use a mixture of circular-symmetric complex-Gaussian distributions projected on the unit hypersphere for modeling the complex discrete Fourier transform coefficients of noisy speech signals in the frequency domain. According to the derived mixture model, clustering of the noise and the target speech components is performed depending on their direction of arrival. A soft masking strategy is proposed for speech enhancement based on responsibilities assigned to the target speech class in each time-frequency bin. Our experimental results show that the proposed approach is more robust than conventional dual-channel noise reduction systems based on the single- and dual-channel noise power spectral density estimators.