Knowledge Commons of Institute of Automation,CAS
Online Audio-Visual Speech Separation with Generative Adversarial Training | |
Zhang Peng1,2; Xu Jiaming1,2; Hao Yunzhe1; Xu Bo1,2,3 | |
2021 | |
会议名称 | the 7th International Conference on COMPUTING AND ARTIFICIAL INTELLIGENCE |
会议录名称 | 0 |
卷号 | 0 |
期号 | 0 |
页码 | 0 |
会议日期 | 2021-4-23 |
会议地点 | 线上会议 |
会议录编者/会议主办者 | Tiangong University |
出版地 | 0 |
出版者 | 0 |
产权排序 | 1 |
摘要 | Audio-visual speech separation has been demonstrated to be effective in solving the cocktail party problem. However, most of the models cannot meet online processing, which limits their application in video communication and human-robot interaction. Besides, SI-SNR, the most popular training loss function in speech separation, results in some artifacts in the separated audio, which would harm downstream applications, such as automatic speech recognition (ASR). In this paper, we propose an online audio-visual speech separation model with generative adversarial training to solve the two problems mentioned above. We build our generator (i.e., audio-visual speech separator) with causal temporal convolutional network block and propose a streaming inference strategy, which allows our model to do speech separation in an online manner. The discriminator is involved in optimizing the generator, which can reduce the negative effects of SI-SNR. Experiments on simulated 2-speaker mixtures based on challenging audio-visual dataset LRS2 show that our model outperforms the state-of-the-art audio-only model Conv-TasNet and audio-visual model advr-AVSS under the same model size. We test the running time of our model on GPU and CPU, and results show that our model meets online processing. The demo and code can be found at https://github.com/aispeech-lab/oavss. |
关键词 | audio-visual speech separation online processing generative adversarial training causal temporal convolutional network |
学科门类 | 工学 |
DOI | 0 |
URL | 查看原文 |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 语音识别与合成 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44911 |
专题 | 复杂系统认知与决策实验室_听觉模型与认知计算 中国科学院自动化研究所 |
通讯作者 | Xu Jiaming; Xu Bo |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Science 3.Center for Excellence in Brain Science and Intelligence Technology, CAS |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhang Peng,Xu Jiaming,Hao Yunzhe,et al. Online Audio-Visual Speech Separation with Generative Adversarial Training[C]//Tiangong University. 0:0,2021:0. |
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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 | ||
Online Audio-visual (532KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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