Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 61, Issue 3, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2012.2229991
Keywords
Function approximation; noise reduction; regularization; reproducing kernel Hilbert space; speech distortion
Categories
Funding
- MASTAR project of the Universal Communication Research Institute of National Institute of Information and Communications Technology, Japan
- Ministry of Education, Culture, Sports, Science and Technology of Japan [22700193]
- Grants-in-Aid for Scientific Research [22700193] Funding Source: KAKEN
Ask authors/readers for more resources
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 speech estimation as a function approximation problem in a regularized reproducing kernel Hilbert space (RKHS). In the estimation, the objective function is formulated to find an approximation function by controlling the tradeoff between approximation accuracy and function complexity. For noisy observations, this is equivalent to controlling the tradeoff between noise reduction and speech distortion. Since the target function is approximated in an RKHS, either a linear or nonlinear mapping function can be naturally incorporated in the estimation by a kernel trick. Traditional signal subspace and Wiener filtering based noise reduction can be derived as special cases when a linear kernel function is applied in this framework. We first provided a theoretical analysis of the tradeoff property of the framework in noise reduction. Then we applied our proposed noise reduction method in speech enhancement and noisy robust speech recognition experiments. Compared to several classical noise reduction methods, our proposed method showed promising advantages.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available