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Multiple Sound Source Localization With Steered Response Power Density and Hierarchical Grid Refinement

机译:具有转向响应功率密度和分级网格细化的多声源定位

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Estimation of the direction-of-arrival (DOA) of sound sources is an important step in sound field analysis. Rigid spherical microphone arrays allow the calculation of a compact spherical harmonic representation of the sound field. The standard method for analyzing sound fields recorded using such arrays is steered response power (SRP) maps wherein the source DOA can be estimated as the steering direction that maximizes the output power of a maximally directive beam. This approach is computationally costly since it requires steering the beam in all possible directions. This paper presents an extension to SRP called steered response power density (SRPD) and an associated, signal-adaptive search method called hierarchical grid refinement for reducing the number of steering directions needed for DOA estimation. The proposed method can localize near-coherent as well as incoherent sources while jointly providing the number of prominent sources in the scene. It is shown to be robust to reverberation and additive white noise. An evaluation of the proposed method using simulations and real recordings under highly reverberant conditions as well as a comparison with the state-of-the-art methods are presented.
机译:声源到达方向(DOA)的估计是声场分析中的重要一步。刚性球形麦克风阵列允许计算声场的紧凑球形谐波表示。用于分析使用此类阵列记录的声场的标准方法是转向响应功率(SRP)映射,其中可以将源DOA估计为使最大定向波束的输出功率最大化的转向方向。该方法在计算上是昂贵的,因为它需要在所有可能的方向上操纵光束。本文介绍了SRP的扩展,称为转向响应功率密度(SRPD),以及一种相关的信号自适应搜索方法,称为分层网格优化,用于减少DOA估计所需的转向方向数量。所提出的方法可以定位近相干和非相干源,同时联合提供场景中重要源的数量。它显示出对混响和附加白噪声的鲁棒性。提出了在高混响条件下使用模拟和真实录音对拟议方法进行的评估,并与最新技术进行了比较。

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