Research School of Engineering, College of Engineering and Computer Science, Australian National University, Canberra, Australia Capital Territory 0200, Australia.
Department of Electrical and Computer Engineering, University of Auckland, Auckland 1142, New Zealand.
J Acoust Soc Am. 2014 Mar;135(3):1207-17. doi: 10.1121/1.4864304.
The spectral localization cues contained in the head-related transfer function are known to play a contributory role in the sound source localization abilities of humans. However, existing localization techniques are unable to fully exploit this diversity to accurately localize a sound source. The availability of just two measured signals complicates matters further, and results in front to back confusions and poor performance distinguishing between the source locations in a vertical plane. This study evaluates the performance of a source location estimator that retains the frequency domain diversity of the head-related transfer function. First, a method for extracting the directional information in the subbands of a broadband signal is described, and a composite estimator based on signal subspace decomposition is introduced. The localization performance is experimentally evaluated for single and multiple source scenarios in the horizontal and vertical planes. The proposed estimator's ability to successfully localize a sound source and resolve the ambiguities in the vertical plane is demonstrated, and the impact of the source location, knowledge of the source and the effect of reverberation is discussed.
头部相关传递函数中包含的频谱定位线索已知在人类声源定位能力中发挥着重要作用。然而,现有的定位技术无法充分利用这种多样性来准确地定位声源。仅有的两个测量信号使得情况更加复杂,导致前后混淆,并且在垂直平面上区分声源位置的性能较差。本研究评估了一种保留头部相关传递函数频域多样性的声源定位估计器的性能。首先,描述了一种从宽带信号子带中提取方向信息的方法,并引入了一种基于信号子空间分解的组合估计器。在水平和垂直平面上的单声源和多声源场景中,对定位性能进行了实验评估。所提出的估计器能够成功地定位声源并解决垂直平面中的歧义,讨论了声源位置、声源知识和混响效应的影响。