Title
Study of the Noise-Reduction Problem in the Karhunen–LoÈve Expansion Domain
Abstract
Noise reduction, which aims at estimating a clean speech from a noisy observation, has long been an active research area. The standard approach to this problem is to obtain the clean speech estimate by linearly filtering the noisy signal. The core issue, then, becomes how to design an optimal linear filter that can significantly suppress noise without introducing perceptually noticeable speech distortion. Traditionally, the optimal noise-reduction filters are formulated in either the time or the frequency domains. This paper studies the problem in the Karhunen-Loeve expansion domain. We develop two classes of optimal filters. The first class achieves a frame of speech estimate by filtering the corresponding frame of the noisy speech. We will show that many existing methods such as the widely used Wiener filter and subspace technique are closely related to this category. The second class obtains noise reduction by filtering not only the current frame, but also a number of previous consecutive frames of the noisy speech. We will discuss how to design the optimal noise-reduction filters in each class and demonstrate, through both theoretical analysis and experiments, the properties of the deduced optimal filters.
Year
DOI
Venue
2009
10.1109/TASL.2009.2014793
IEEE Transactions on Audio, Speech & Language Processing
Keywords
Field
DocType
optimal noise-reduction filter,optimal linear filter,noisy observation,deduced optimal filter,noisy speech,noise-reduction problem,optimal filter,perceptually noticeable speech distortion,speech estimate,expansion domain,clean speech estimate,clean speech,frequency domain,noise reduction,signal to noise ratio,speech processing,noise measurement,pearson correlation coefficient,signal processing,filtering,wiener filter,linear filtering,frequency domain analysis
Wiener filter,Noise reduction,Speech enhancement,Speech processing,Signal processing,Noise measurement,Linear filter,Computer science,Filter (signal processing),Speech recognition
Journal
Volume
Issue
ISSN
17
4
1558-7916
Citations 
PageRank 
References 
12
0.83
19
Authors
3
Name
Order
Citations
PageRank
Jingdong Chen11460128.79
Jacob Benesty21941146.01
Yiteng Huang3123998.26