学习神经网络声音的跟踪Learning to localise sounds with spiking neural networks |
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课程网址: | http://videolectures.net/nips2010_goodman_lls/ |
主讲教师: | Dan F. Goodman |
开课单位: | 法国高等师范学院 |
开课时间: | 2011-03-25 |
课程语种: | 英语 |
中文简介: | 为了定位声音的来源,我们使用由两个耳朵接收的信号的位置特定属性,这是由我们的头部和耳廓对头部相关传递函数(HRTF)对原始声音的不对称滤波引起的。例如,这些HRTF在整个生物体的生命周期中发生变化,因此所需的神经回路不能完全硬连线。由于HRTF无法直接从感知体验中获取,因此只能从过滤后的声音中推断出它们。我们提出了一种基于提取位置特定同步模式的声音定位的尖峰神经网络模型,以及一种简单的监督算法,用于从一组示例声音中学习同步模式和位置之间的映射,而不具有HRTF的先前知识。在学习之后,我们的模型能够准确地定位方位角和仰角中的新声音,包括区分来自前后声音的困难任务。 |
课程简介: | To localise the source of a sound, we use location-specific properties of the signals received at the two ears caused by the asymmetric filtering of the original sound by our head and pinnae, the head-related transfer functions (HRTFs). These HRTFs change throughout an organism's lifetime, during development for example, and so the required neural circuitry cannot be entirely hardwired. Since HRTFs are not directly accessible from perceptual experience, they can only be inferred from filtered sounds. We present a spiking neural network model of sound localisation based on extracting location-specific synchrony patterns, and a simple supervised algorithm to learn the mapping between synchrony patterns and locations from a set of example sounds, with no previous knowledge of HRTFs. After learning, our model was able to accurately localise new sounds in both azimuth and elevation, including the difficult task of distinguishing sounds coming from the front and back. |
关 键 词: | 定位声音; 神经回路; 神经回路 |
课程来源: | 视频讲座网 |
最后编审: | 2020-01-13:chenxin |
阅读次数: | 41 |