The steered response power phase transform (SRP-PHAT) is a well-known algorithm for acoustic source localization using microphone arrays. It consists in the computation of the generalized cross-correlation (GCC) between each microphone pair, and in the coherent summation of the GCC values in the grid search space. Several improvements based on the volumetric grid have been proposed in order to achieve spatial resolution scalability and to reduce the computational cost by using a coarser grid. In general, the problem of the volumetric based methods is that the noise and the reverberation are projected into the search space since all GCC information is used to build the acoustic map. It is hence proposed a volumetric grid SRP-PHAT algorithm based on the geometrically sampled grid (GSG) that incorporates a max-pooling (MP) operation in the volume accumulation of the GCC values in order to improve the localization performance. The MP is the solution of a minimization-maximization problem that aims at minimizing the deleterious effect of noise and reverberation and at maximizing the accuracy of the GCC values related to the target sound source. Simulations and real-world experiments demonstrate the efficiency of the proposed SRP-GSG-MP algorithm in adverse conditions.

中文翻译：

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使用具有最大池操作的几何采样网格 SRP-PHAT 算法进行声源定位

转向响应功率相位变换 (SRP-PHAT) 是一种众所周知的使用麦克风阵列的声源定位算法。它包括计算每个麦克风对之间的广义互相关 (GCC)，以及网格搜索空间中 GCC 值的相干求和。已经提出了一些基于体积网格的改进，以实现空间分辨率可扩展性并通过使用更粗略的网格来降低计算成本。一般来说，基于体积的方法的问题是噪声和混响被投射到搜索空间中，因为所有 GCC 信息都用于构建声学图。因此，提出了一种基于几何采样网格（GSG）的体积网格 SRP-PHAT 算法，该算法在 GCC 值的体积累积中结合了最大池（MP）操作，以提高定位性能。MP 是最小化-最大化问题的解决方案，旨在最小化噪声和混响的有害影响并最大化与目标声源相关的 GCC 值的准确性。模拟和实际实验证明了所提出的 SRP-GSG-MP 算法在不利条件下的效率。MP 是最小化-最大化问题的解决方案，旨在最小化噪声和混响的有害影响并最大化与目标声源相关的 GCC 值的准确性。模拟和实际实验证明了所提出的 SRP-GSG-MP 算法在不利条件下的效率。MP 是最小化-最大化问题的解决方案，旨在最小化噪声和混响的有害影响并最大化与目标声源相关的 GCC 值的准确性。模拟和实际实验证明了所提出的 SRP-GSG-MP 算法在不利条件下的效率。