听觉源侧化的脉冲神经网络方法

R. Luke, D. McAlpine
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引用次数: 3

摘要

提出了一种基于尖峰神经网络的多传声器声源定位方法。我们证明了连接到尖峰神经网络的两个麦克风系统可以完全基于麦克风间的时间差异来定位声源,而不需要手动配置延迟线。提供了一个双传感器示例,其包括1)将声信号转换为一系列尖峰的前端,2)尖峰神经元的隐藏层,3)表示声源位置的尖峰神经元的输出层。我们详细介绍了网络的训练,以及在安静和噪声条件下对其性能的评估。该系统在两个位置进行了训练,结果表明,在安静的条件下,当提供以前未见过的数据时,侧向化精度达到100%。我们还证明了网络可以泛化到调制速率和背景噪声,而不是它所训练的。
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A Spiking Neural Network Approach to Auditory Source Lateralisation
A novel approach to multi-microphone acoustic source localisation based on spiking neural networks is presented. We demonstrate that a two microphone system connected to a spiking neural network can be used to localise acoustic sources based purely on inter microphone timing differences, with no need for manually configured delay lines. A two sensor example is provided which includes 1) a front end which converts the acoustic signal to a series of spikes, 2) a hidden layer of spiking neurons, 3) an output layer of spiking neurons which represents the location of the acoustic source. We present details on training the network, and evaluation of its performance in quiet and noisy conditions. The system is trained on two locations, and we show that the lateralisation accuracy is 100% when presented with previously unseen data in quiet conditions. We also demonstrate the network generalises to modulation rates and background noise on which it was not trained.
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