New insights into the noise reduction Wiener filter
New insights into the noise reduction Wiener filter
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DOI：
10.1109/tsa.2005.860851
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发表时间：
2006-07-01
影响因子：
--
通讯作者：
Doclo, Simon
中科院分区：
文献类型：
--
作者：
Chen, Jingdong;Benesty, Jacob;Doclo, Simon
在过去的几十年里，降噪问题引起了相当多的研究关注。在众多的降噪技术中，最优维纳滤波可以被认为是最基本的降噪方法之一，它已经以不同的形式被描述出来，并在不同的应用中被采用。虽然维纳滤波可能会对语音信号造成一些不利影响(质量或可理解性明显或显著下降)已不是什么秘密，但很少有报道表明降噪和语音失真之间的内在关系。通过定义一个语音失真指数来衡量语音信号的变形程度，并定义两个降噪因子来量化被衰减的噪声量，研究了维纳滤波在降噪背景下的定量性能行为。我们证明了在单通道情况下，后验信噪比(定义在维纳滤波之后)大于或等于先验信噪比(定义在维纳滤波之前)，这表明维纳滤波总是能够实现降噪。然而，降噪量一般与语音降级量成正比。这可能看起来令人沮丧，因为我们总是希望算法具有最大的降噪效果，而不会有太多的语音失真。幸运的是，我们展示了可以通过三种不同的方法更好地管理语音失真。如果我们对干净的语音信号有一些先验知识(如线性预测系数)，则可以利用这种先验知识来实现去噪，同时保持较低的语音失真水平。在没有先验知识的情况下，通过适当地操作维纳滤波器，仍然可以更好地控制噪声抑制和语音失真，从而得到次优的维纳滤波器。在我们有多个麦克风传感器的情况下，语音信号的多次观测可以用来在较少甚至没有语音失真的情况下降低噪声。
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 multiple microphone sensors, the multiple observations of the speech signal can be used to reduce noise with less or even no speech distortion.