Multimedia and Multimodal Processing Research Group, University of Jaén, 23700, Linares, Spain.
J Acoust Soc Am. 2012 Sep;132(3):1257-60. doi: 10.1121/1.4740489.
The localization of active speakers with microphone arrays is an active research line with a considerable interest in many acoustic areas. Many algorithms for source localization are based on the computation of the Generalized Cross-Correlation function between microphone pairs employing phase transform weighting. Unfortunately, the performance of these methods is severely reduced when wall reflections and multiple sound sources are present in the acoustic environment. As a result, estimating the number of active sound sources and their actual directions becomes a challenging task. To effectively tackle this problem, a Bayesian inference framework is proposed. Based on a nested sampling algorithm, a mixture model and its parameters are estimated, indicating both the number of sources-model selection-and their angle of arrival-parameter estimation, respectively. A set of measured data demonstrates the accuracy of the proposed model.
使用麦克风阵列进行活动发言人定位是一个活跃的研究领域，在许多声学领域都有相当大的兴趣。许多声源定位算法都是基于对麦克风对之间的广义互相关函数进行计算，并采用相位变换加权。不幸的是，当声学环境中存在墙壁反射和多个声源时，这些方法的性能会严重降低。因此，估计活动声源的数量及其实际方向成为一项具有挑战性的任务。为了有效地解决这个问题，提出了一种贝叶斯推理框架。基于嵌套抽样算法，对混合模型及其参数进行估计，分别表示源的数量-模型选择-和它们的到达角-参数估计。一组测量数据证明了所提出模型的准确性。
