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An Alternative Model for Sound Signals Encountered in Reverberant Environments; Robust Maximum Likelihood Localization and Parameter Estimation Based on a Sub-Gaussian Model

机译:混响环境中遇到的声音信号的替代模型;基于次高斯模型的鲁棒最大似然定位和参数估计

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In this paper we investigate an alternative to the Gaussian density for modeling signals encountered in audioenvironments. The observation that sound signals are impulsive in nature, combined with the reverberatione?ects commonly encountered in audio, motivates the use of the Sub-Gaussian density.The new Sub-Gaussian statistical model and the separable solution of its Maximum Likelihood estimatorare derived. These are used in an array scenario to demonstrate with both simulations and two di?erentmicrophone arrays the achievable performance gains.The simulations exhibit the robustness of the sub-Gaussian based method while the real world experimentsreveal a signiˉcant performance gain, supporting the claim that the sub-Gaussian model is better suited forsound signals.
机译:在本文中,我们研究了高斯密度的一种替代方法,用于对音频中遇到的信号进行建模 环境。声音信号本质上是脉冲性的,结合了混响的观察 音频中常见的影响会激发次高斯密度的使用。 新的次高斯统计模型及其最大似然估计的可分解 派生。这些用于数组方案中,以通过仿真和两个不同来演示 麦克风阵列可实现的性能提升。 仿真展示了基于次高斯的方法的鲁棒性,而实际实验中 揭示了显着的性能提升,支持了亚高斯模型更适合于 声音信号。

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