@inbook{b4b06160b84e49cbbf5ca0f899c43aea,
title = "Optimal filters in the transform domain",
abstract = "In this chapter, we reformulate the noise reduction problem into a more generalized transform domain. There are at least two advantages doing this: first, different transforms can be used to replace each other without any requirement to change the algorithm formulation (optimal or suboptimal filter) and second, it is easier to fairly compare different transforms for their noise reduction performance. To do so, we need to generalize the KLE concept. Therefore, we recommend the reader to be familiar with the KLE (explained in Chapter 2) before reading this part.",
keywords = "Filter Design, Frame Length, Noise Reduction, White Gaussian Noise, Wiener Filter",
author = "Jacob Benesty and Jingdong Chen and Yiteng Huang and Israel Cohen",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2009.",
year = "2009",
doi = "10.1007/978-3-642-00296-0\_10",
language = "英语",
series = "Springer Topics in Signal Processing",
publisher = "Springer Science and Business Media B.V.",
pages = "1--30",
booktitle = "Springer Topics in Signal Processing",
}