Knowledge Commons of Institute of Automation,CAS
Exploiting Spectro-temporal Structures Using NMF For DNN-based Supervised Speech Separation | |
Nie S(聂帅)1; Shan Liang1; Hao Li2; XueLiang Zhang2; ZhanLei Yang1; WenJu Liu1 | |
2016-03 | |
会议名称 | IEEE International Conference on Acoustics, Speech and Signal Processing |
会议日期 | 2016-3-20~2016-3-25 |
会议地点 | Shanghai, China |
摘要 | The targets of speech separation, whether ideal masks or magnitude spectrograms of interest, have prominent spectro-temporal structures. These characteristics are very worthy to be exploited for speech separation, however, they are usually ignored in previous works. In this paper, we use nonnegative matrix factorization (NMF) to exploit the spectro-temporal structures of magnitude spectrograms. With nonnegative constrains, NMF can capture the basis spectra patterns of speech and noise. Then the learned basis spectra are integrated into a deep neural network (DNN) to reconstruct the magnitude spectrograms of speech and noise with their nonnegative linear combination. Using the reconstructed spectrograms, we further explore a discriminative training objective and a joint optimization framework for the proposed model. Systematic experiments show that the proposed model is competitive with the previous methods in monaural speech separation tasks. |
其他摘要 |
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语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40646 |
专题 | 多模态人工智能系统全国重点实验室_智能交互 |
通讯作者 | Nie S(聂帅) |
作者单位 | 1.中国科学院自动化研究所 2.内蒙古大学 |
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 | Nie S,Shan Liang,Hao Li,et al. Exploiting Spectro-temporal Structures Using NMF For DNN-based Supervised Speech Separation[C],2016. |
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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 | ||
ICASSP-2016.pdf(357KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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