Knowledge Commons of Institute of Automation,CAS
Modelling Speaker-dependent Auditory Attention Using A Spiking Neural Network with Temporal Coding and Supervised Learning | |
Yating Huang1,2; Jiaming Xu1; Bo Xu1,2,3 | |
2019-12 | |
会议名称 | The 26th International Conference on Neural Information Processing (ICONIP 2019) |
会议日期 | December 12-15, 2019 |
会议地点 | Sydney, Australia |
摘要 | Spiking Neural Networks (SNNs) are regarded as the third generation of neural network models, which can learn the precise spike trains of the stimuli. As speech signals exhibit strong temporal structure, SNNs are a natural choice for learning temporal dynamics of the speech. Therefore, we propose a unified biologically plausible framework using spiking neurons with temporal coding and supervised learning to solve the auditory attention problem. We further introduce momentum and Nesterov's accelerated gradient into the Remote Supervised Method to improve the performance and speed up the spike train learning. We evaluate our model on Grid corpus and demonstrate that our model performs a precise spike train coding for auditory attention and outperforms the baseline artificial neural networks. |
收录类别 | EI |
七大方向——子方向分类 | 类脑模型与计算 |
国重实验室规划方向分类 | 语音语言处理 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49727 |
专题 | 复杂系统认知与决策实验室_听觉模型与认知计算 |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.University of Chinese Academy of Sciences, Beijing, China 3.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Yating Huang,Jiaming Xu,Bo Xu. Modelling Speaker-dependent Auditory Attention Using A Spiking Neural Network with Temporal Coding and Supervised Learning[C],2019. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
ICONIP2019_Modelling(3377KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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