Acoustic Source Localization Based on Geometric Projection in Reverberant and Noisy Environments
Acoustic Source Localization Based on Geometric Projection in Reverberant and Noisy Environments
复制标题
混响和噪声环境中基于几何投影的声源定位
DOI：
10.1109/jstsp.2018.2885410
复制
发表时间：
2019-03-01
影响因子：
7.5
通讯作者：
Cohen, Israel
中科院分区：
文献类型：
--
作者：
Long, Tao;Chen, Jingdong;Cohen, Israel
声源定位（ASL）在声音采集、语音通信和人机界面中是一个基本但仍具挑战性的信号处理问题。许多ASL算法已经被开发出来，例如导向响应功率（SRP）、SRP - 相位变换、最小方差无失真响应、多信号分类（MUSIC）、基于豪斯霍尔德变换的方法等等。这些算法中的大多数需要数百甚至数千个样本才能产生一个可靠的估计，这使得它们难以跟踪移动的声源。此外，文献中很少有研究致力于展示这些方法之间的内在联系。本文处理ASL问题，重点关注如何利用一小段声学信号帧（在频域中对应于单个样本）实现ASL。它从几何投影的角度重新阐述了ASL问题。提出了四种类型的功率函数，从而产生了几种不同的ASL算法。通过分析这些功率函数，我们展示了常用的传统算法与我们的方法之间的等效性，这为传统算法提供了一些新的见解。讨论了不同算法之间的关系，这使得容易理解每种方法的优缺点。在实际声学环境中的实验证实了理论分析，这反过来证明了本文的贡献。
Acoustic source localization (ASL) is a fundamental yet still challenging signal processing problem in sound acquisition, speech communication, and human-machine interfaces. Many ASL algorithms have been developed, such us the steered response power (SRP), the SRP-phase transform, the minimum variance distortionless response, the multiple signal classification (MUSIC), the householder transform-based methods, to name but a few. Most of those algorithms require hundreds or even thousands of snapshots to produce one reliable estimate, which make them difficult to track moving sources. Moreover, not much efforts have been reported in the literature to show the intrinsic relationships among those methods. This paper deals with the ASL problem with its focal point placed on how to achieve ASL with a short frame of acoustic signal (corresponding to a single snapshot in the frequency domain). It reformulates the ASL problem from the perspective of geometric projection. Four types of' power functions are proposed, leading to several different algorithms for ASL. By analyzing those power functions, we show the equivalence between the popularly used conventional algorithms and our methods, which provides some new insights into the conventional algorithms. The relationships among different algorithms are discussed, which make it easy to comprehend the pros and cons of each of those methods. Experiments in real acoustic environments corroborate the theoretical analysis, which in turn justifies the contribution of this paper.