Performance of the SDW-MWF With Randomly Located Microphones in a Reverberant Enclosure

S. M. Golan, S. Gannot, I. Cohen
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引用次数: 24

Abstract

Beamforming with wireless acoustic sensor networks (WASNs) has recently drawn the attention of the research community. As the number of microphones grows it is difficult, and in some applications impossible, to determine their layout beforehand. A common practice in analyzing the expected performance is to utilize statistical considerations. In the current contribution, we consider applying the speech distortion weighted multi-channel Wiener filter (SDW-MWF) to enhance a desired source propagating in a reverberant enclosure where the microphones are randomly located with a uniform distribution. Two noise fields are considered, namely, multiple coherent interference signals and a diffuse sound field. Utilizing the statistics of the acoustic transfer function (ATF), we derive a statistical model for two important criteria of the beamformer (BF): the signal to interference ratio (SIR), and the white noise gain. Moreover, we propose reliability functions, which determine the probability of the SIR and white noise gain to exceed a predefined level. We verify the proposed model with an extensive simulative study.
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混响罩中随机位置麦克风的SDW-MWF性能
无线声传感器网络波束形成技术近年来引起了研究界的广泛关注。随着麦克风数量的增加,预先确定它们的布局变得很困难,在某些应用中是不可能的。分析预期性能的一个常见做法是利用统计因素。在当前的贡献中,我们考虑应用语音失真加权多通道维纳滤波器(SDW-MWF)来增强在麦克风随机分布均匀的混响罩中传播的期望源。考虑了两个噪声场,即多个相干干扰信号和一个漫射声场。利用声传递函数(ATF)的统计特性,推导了波束形成器(BF)的两个重要指标的统计模型:信干扰比(SIR)和白噪声增益。此外,我们提出了可靠性函数,它确定SIR和白噪声增益超过预定义水平的概率。我们通过广泛的模拟研究验证了所提出的模型。
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来源期刊
IEEE Transactions on Audio Speech and Language Processing
IEEE Transactions on Audio Speech and Language Processing 工程技术-工程:电子与电气
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审稿时长
24.0 months
期刊介绍: The IEEE Transactions on Audio, Speech and Language Processing covers the sciences, technologies and applications relating to the analysis, coding, enhancement, recognition and synthesis of audio, music, speech and language. In particular, audio processing also covers auditory modeling, acoustic modeling and source separation. Speech processing also covers speech production and perception, adaptation, lexical modeling and speaker recognition. Language processing also covers spoken language understanding, translation, summarization, mining, general language modeling, as well as spoken dialog systems.
期刊最新文献
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