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Multiple source localization for real-world systems.

机译：实际系统的多源本地化。

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摘要

Much research has been published on source localization in a theoretical or laboratory setting. Source localization is very important for several applications, including beam-forming. However it has not been applied widely in practical situations. This work will examine the problems faced by source localization in real acoustical settings and will suggest an algorithm that can robustly estimate locations quickly. Additionally this work will propose a new algorithm that can localize multiple simultaneous sources.; Real audio environments contain significant reverberation and reflections that complicate localization of sources. Strong reflections can be more energetic than the signal arriving via the direct path. This adversely effects algorithms based on time delay estimates. However, more robust methods, like Steered Response Pattern - Phase Transform (SRP-PHAT), tend to be too expensive for the applications envisioned in this paper. This work will quantify the computational complexity of SRP-PHAT to search the space of possible locations.; An alternative algorithm, Hybrid Localization, will be presented that combines two different algorithms to achieve faster computation. It works very well for single source localization and can easily be extended to multiple sources. The multiple source extension makes use of pre-defined sub-arrays and a neural network. Hybrid Localization achieves this reduction in computation time without degrading localization robustness.; While researching Hybrid Localization, a new statistical model was developed to predict the resulting general cross-correlation (GCC) phase transform (PHAT). The model improves on existing models by changing assumptions on the source signals to better match reality. It can be used to quickly simulate the effectiveness of a room, a microphone array geometry, or a localization. In addition it can make predictions for a source in motion.
机译：在理论或实验室环境中，已经有很多关于源定位的研究发表。源定位对于包括波束形成在内的多种应用非常重要。但是，它在实际情况中并未得到广泛应用。这项工作将研究真实声学环境中声源定位所面临的问题，并提出一种可以快速可靠地估计位置的算法。另外，这项工作将提出一种可以定位多个同时源的新算法。真实的音频环境包含大量的混响和反射，使信号源的定位复杂化。比通过直接路径到达的信号，强反射的能量更高。这会对基于时间延迟估计的算法产生不利影响。但是，更健壮的方法（如转向响应模式-相变（SRP-PHAT））对于本文设想的应用来说往往过于昂贵。这项工作将量化SRP-PHAT搜索可能位置空间的计算复杂性。将提出一种替代算法“混合定位”，该算法结合了两种不同的算法以实现更快的计算。它对于单个源本地化非常有效，并且可以轻松扩展到多个源。多源扩展使用预定义的子数组和神经网络。混合本地化可减少计算时间，而不会降低本地化的鲁棒性。在研究混合本地化的同时，开发了一种新的统计模型来预测所得的通用互相关（GCC）相变（PHAT）。该模型通过更改源信号的假设以更好地匹配实际情况，对现有模型进行了改进。它可用于快速模拟房间，麦克风阵列的几何形状或位置的有效性。另外，它可以对运动中的源进行预测。

著录项

  • 作者

    Peterson, John Michael.;

  • 作者单位

    University of Southern California.;

  • 授予单位 University of Southern California.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 160 p.
  • 总页数 160
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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