Controlling the tradeoff property in a regularization framework for noise reduction

The tradeoff between noise reduction and speech distortion is a key concern in designing noise reduction algorithms. We have proposed a regularization framework for noise reduction with the consideration of the tradeoff problem. We regard speech estimation as a functional approximation problem in a reproducing kernel Hilbert space (RKHS). In the estimation, the objective function is formulated to find an approximation function that gives a good tradeoff between the approximation accuracy and complexity of the function. By using a regularization method, the approximation function can be estimated from noisy observations. In this paper, we further provided a theoretical analysis of the tradeoff property of the framework in noise reduction. We applied the framework for speech enhancement experiments in real applications. Compared with several classical noise reduction methods, the proposed framework showed promising advantages.