Knowledge Commons of Institute of Automation,CAS
Towards Modeling Auditory Restoration in Noisy Environments | |
Yating Huang1,2![]() ![]() ![]() ![]() | |
2021-07 | |
会议名称 | IJCNN 2021 : International Joint Conference on Neural Networks |
会议日期 | Jul 18, 2021 |
会议地点 | 线上会议 |
摘要 | Real-world sounds are often interrupted by various kinds of noise. The target signal of the mixture sounds is often degraded or lost. While the human auditory system can extract the target signal from the mixture and restore the degraded or lost parts simultaneously, current computational models often simplify the complex scenarios, which leads to two individual tasks, audio inpainting and speech enhancement. In this work, we take a pioneering step towards modeling auditory restoration, that is to restore the target speech signal, in which there are missing parts in the target signal and the target signal is interfered by background noise. Different from the speech enhancement task, we attempt to fill in the missing gaps with the existence of background noise. Different from the auditory inpainting task, there is some noise in our input signal and the positions of the missing gaps are unknown. In other words, we attempt to reduce interference and restore missing gaps simultaneously. We propose Hourglass-shaped Convolutional Recurrent Network (HCRN) trained with Spectro-Temporal loss to restore the target signal from the incomplete noisy mixture. Moreover, instead of restoring non-human sounds, we focus on speech restoration, which poses more challenges on reconstruction. Both the quantitative and qualitative performance show that our proposed method can suppress the background noise, identify and restore the missing gaps of the salient signal with the unreliable context information. Our code is available in https://github.com/aispeech-lab/HCRN. |
收录类别 | EI |
语种 | 英语 |
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/173211/49725 |
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 (CASIA), Beijing, China 2.School of Future Technology, University of Chinese Academy of Sciences, Beijing, China 3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 4.Center for Excellence in Brain Science and Intelligence Technology, CAS, 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,Yunzhe Hao,Jiaming Xu,et al. Towards Modeling Auditory Restoration in Noisy Environments[C],2021. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
IJCNN2021_Towards_Mo(628KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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