论文标题
基于空间相干的RTF矢量估计的偏置分析弥漫性声场中的声学传感器网络
Bias Analysis of Spatial Coherence-Based RTF Vector Estimation for Acoustic Sensor Networks in a Diffuse Sound Field
论文作者
论文摘要
在许多多微粒算法中，需要对所需扬声器的相对传递函数（RTF）进行估计。最近，假设局部麦克风阵列和多个外部麦克风之间的噪声分量的空间相干性（SC）较低，则提出了一种计算有效的RTF矢量估计方法。为了优化输出信号噪声比（SNR），该方法线性结合了多个RTF矢量估计，其中使用广义特征值分解（GEVD）计算复杂值的权重。在本文中，我们对具有多个外部麦克风的基于SC的RTF矢量估计方法进行了理论偏差分析。假设噪声场的某个模型，我们得出了权重的分析表达式，表明基于最佳模型的权重是实现的，并且仅取决于外部麦克风中的输入SNR。带有现实世界记录的仿真显示了基于GEVD和基于模型的权重的良好性。但是，结果还表明，在实践中，基于模型的权重无法解释的估计错误。
In many multi-microphone algorithms, an estimate of the relative transfer functions (RTFs) of the desired speaker is required. Recently, a computationally efficient RTF vector estimation method was proposed for acoustic sensor networks, assuming that the spatial coherence (SC) of the noise component between a local microphone array and multiple external microphones is low. Aiming at optimizing the output signal-to-noise ratio (SNR), this method linearly combines multiple RTF vector estimates, where the complex-valued weights are computed using a generalized eigenvalue decomposition (GEVD). In this paper, we perform a theoretical bias analysis for the SC-based RTF vector estimation method with multiple external microphones. Assuming a certain model for the noise field, we derive an analytical expression for the weights, showing that the optimal model-based weights are real-valued and only depend on the input SNR in the external microphones. Simulations with real-world recordings show a good accordance of the GEVD-based and the model-based weights. Nevertheless, the results also indicate that in practice, estimation errors occur which the model-based weights cannot account for.
