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Noise reduction algorithms are widely used to mitigate noise effects on speech to improve the robustness of speech technology applications. However, they inevitably cause speech distortion. The tradeoff between noise reduction and speech distortion is a key concern in designing noise reduction algorithms. This study proposes a novel framework for noise reduction by considering this tradeoff. We regard...
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...
Noise reduction is used to reduce the noise effect on speech, and is important for many real speech applications. However, noise reduction inevitably causes speech distortion. The trade-off between noise reduction and speech distortion is always a key concern in designing noise reduction algorithms. In this study, we took a new look at this problem, and regarded the speech estimation as a functional...
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