Chan Vincent Yue-Sek, Jin Craig T, van Schaik André
School of Electrical and Information Engineering, The University of Sydney Sydney, NSW, Australia.
Front Neurosci. 2010 Nov 29;4:196. doi: 10.3389/fnins.2010.00196. eCollection 2010.
A neuromorphic sound localization system is presented. It employs two microphones and a pair of silicon cochleae with address event interface for front-end processing. The system is based the extraction of interaural time difference from a far-field source. At each frequency channel, a soft-winner-takes-all network is used to preserve timing information before it is processed by a simple neural network to estimate auditory activity at all bearing positions. The estimates are then combined across channels to produce the final estimate. The proposed algorithm is adaptive and supports online learning, enabling the system to compensate for circuit mismatch and environmental changes. Its localization capability was tested with white noise and pure tone stimuli, with an average error of around 3° in the -45° to 45° range.
提出了一种神经形态声音定位系统。它采用两个麦克风和一对带有地址事件接口的硅耳蜗进行前端处理。该系统基于从远场源提取双耳时间差。在每个频率通道，使用一个软胜者全得网络来保留时间信息，然后由一个简单神经网络对其进行处理，以估计所有方位位置的听觉活动。然后跨通道组合这些估计值以产生最终估计。所提出的算法具有自适应性并支持在线学习，使系统能够补偿电路失配和环境变化。使用白噪声和纯音刺激对其定位能力进行了测试，在-45°至45°范围内平均误差约为3°。
