Name
Abstract | ||
|---|---|---|
The problem of noise reduction has attracted a considerable amount of research attention over the past several decades. Among the numerous techniques that were developed, the optimal Wiener filter can be considered as one of the most fundamental noise reduction approaches, which has been delineated in different forms and adopted in various applications. Although it is not a secret that the Wiener filter may cause some detrimental effects to the speech signal (appreciable or even significant degradation in quality or intelligibility), few efforts have been reported to show the inherent relationship between noise reduction and speech distortion. By defining a speech-distortion index to measure the degree to which the speech signal is deformed and two noise-reduction factors to quantify the amount of noise being attenuated, this paper studies the quantitative performance behavior of the Wiener filter in the context of noise reduction. We show that in the single-channel case the a posteriori signal-to-noise ratio (SNR) (defined after the Wiener filter) is greater than or equal to the a priori SNR (defined before the Wiener filter), indicating that the Wiener filter is always able to achieve noise reduction. However, the amount of noise reduction is in general proportional to the amount of speech degradation. This may seem discouraging as we always expect an algorithm to have maximal noise reduction without much speech distortion. Fortunately, we show that speech distortion can be better managed in three different ways. If we have some a priori knowledge (such as the linear prediction coefficients) of the clean speech signal, this a priori knowledge can be exploited to achieve noise reduction while maintaining a low level of speech distortion. When no a priori knowledge is available, we can still achieve a better control of noise reduction and speech distortion by properly manipulating the Wiener filter, resulting in a suboptimal Wiener filter. In case that we have multi- - ple microphone sensors, the multiple observations of the speech signal can be used to reduce noise with less or even no speech distortion |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/TSA.2005.860851 | IEEE Transactions on Audio, Speech & Language Processing |
Keywords | Field | DocType |
considerable amount,speech signal,maximal noise reduction,clean speech signal,fundamental noise reduction approach,speech distortion,speech degradation,new insight,wiener filter,noise reduction,optimal wiener filter,indexation,speech processing,degradation,distortion,snr,signal to noise ratio,acoustic noise,wiener filtering,automatic speech recognition,a priori knowledge,detectors,sensors | Wiener filter,Speech enhancement,Noise,Speech processing,Median filter,Noise measurement,Computer science,Wiener deconvolution,Salt-and-pepper noise,Speech recognition | Journal |
Volume | Issue | ISSN |
14 | 4 | 1558-7916 |
Citations | PageRank | References |
139 | 7.60 | 31 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingdong Chen | 1 | 1460 | 128.79 |
| Jacob Benesty | 2 | 1941 | 146.01 |
| Yiteng Huang | 3 | 1239 | 98.26 |
| S. Doclo | 4 | 382 | 22.81 |