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Multichannel Eigenspace Beamforming in a Reverberant Noisy Environment With Multiple Interfering Speech Signals

机译:混响嘈杂环境中具有多个干扰语音信号的多通道特征空间波束形成

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In many practical environments we wish to extract several desired speech signals, which are contaminated by nonstationary and stationary interfering signals. The desired signals may also be subject to distortion imposed by the acoustic room impulse responses (RIRs). In this paper, a linearly constrained minimum variance (LCMV) beamformer is designed for extracting the desired signals from multimicrophone measurements. The beamformer satisfies two sets of linear constraints. One set is dedicated to maintaining the desired signals, while the other set is chosen to mitigate both the stationary and nonstationary interferences. Unlike classical beamformers, which approximate the RIRs as delay-only filters, we take into account the entire RIR [or its respective acoustic transfer function (ATF)]. The LCMV beamformer is then reformulated in a generalized sidelobe canceler (GSC) structure, consisting of a fixed beamformer (FBF), blocking matrix (BM), and adaptive noise canceler (ANC). It is shown that for spatially white noise field, the beamformer reduces to a FBF, satisfying the constraint sets, without power minimization. It is shown that the application of the adaptive ANC contributes to interference reduction, but only when the constraint sets are not completely satisfied. We show that relative transfer functions (RTFs), which relate the desired speech sources and the microphones, and a basis for the interference subspace suffice for constructing the beamformer. The RTFs are estimated by applying the generalized eigenvalue decomposition (GEVD) procedure to the power spectral density (PSD) matrices of the received signals and the stationary noise. A basis for the interference subspace is estimated by collecting eigenvectors, calculated in segments where nonstationary interfering sources are active and the desired sources are inactive. The rank of the basis is then reduced by the application of the orthogonal triangular decomposition (QRD). This procedure relaxes the comm-non requirement for nonoverlapping activity periods of the interference sources. A comprehensive experimental study in both simulated and real environments demonstrates the performance of the proposed beamformer.
机译:在许多实际环境中,我们希望提取一些所需的语音信号,这些语音信号会受到非平稳和固定干扰信号的污染。所需信号也可能会受到声学房间脉冲响应(RIR)施加的失真的影响。本文设计了一种线性约束最小方差(LCMV)波束形成器,用于从多麦克风测量中提取所需信号。波束形成器满足两组线性约束。一组专用于维持所需信号,而另一组专用于减轻固定和非固定干扰。与将RIR近似为仅延迟滤波器的经典波束形成器不同,我们将整个RIR [或其相应的声传递函数(ATF)]考虑在内。然后,将LCMV波束形成器重新构造为广义旁瓣消除器(GSC)结构,该结构由固定波束形成器(FBF),阻塞矩阵(BM)和自适应噪声消除器(ANC)组成。结果表明,对于空间白噪声场,波束形成器减少到FBF,满足约束集,而没有最小化功率。结果表明,自适应ANC的应用有助于减少干扰,但前提是不能完全满足约束条件。我们表明,相对传递函数(RTFs)与所需的语音源和麦克风相关,并且干扰子空间的基础足以构建波束形成器。通过将广义特征值分解(GEVD)程序应用于接收信号和固定噪声的功率谱密度(PSD)矩阵,可以估算RTF。通过收集特征向量来估算干扰子空间的基础,特征向量是在非平稳干扰源处于活动状态且所需源处于非活动状态的分段中计算的。然后通过应用正交三角分解(QRD)来降低基础的等级。此过程放宽了对干扰源的不重叠活动时间的通用要求。在模拟和真实环境中进行的全面实验研究证明了所提出的波束形成器的性能。

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