CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
Cross-domain cooperative deep stacking network for speech separation
Wei Jiang1; Shan Liang1; Like Dong2; Hong Yang2; Wenju Liu1; Yunji Wang3
2015
会议名称IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
会议录名称EI
会议日期April 19-24, 2015
会议地点Brisbane, Australia
摘要Nowadays supervised speech separation has drawn much attention and shown great promise in the meantime. While there has been a lot of success, existing algorithms perform the task only in one preselected representative domain. In this study, we propose to perform the task in two different time-frequency domains simultaneously and cooperatively, which can model the implicit correlations between different representations of the same speech separation task. Besides, many time-frequency (T-F) units are dominated by noise in low signal-to-noise ratio (SNR) conditions, so more robust features are obtained by stacking features of original mixtures with that extracted from separated speech of each deep stacking network (DSN) block, which can be regarded as a denoisedversionoftheoriginalfeatures. Quantitativeexperiments show that the proposed cross-domain cooperative deep stacking network (DSN-CDC) has enhanced modeling capability as well as generalization ability, which outperforms a previous algorithm based on standard deep neural networks.
关键词Speech Separation Cross-domain Cooperative Structure Deep Stacking Network Deep Neural Network
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12361
专题模式识别国家重点实验室_机器人视觉
通讯作者Wenju Liu
作者单位1.NLPR, Institute of Automation, Chinese Academy of Sciences
2.Electric Power Research Institute of ShanXi Electric Power Company, China State Grid Corp
3.Electrical and Computer Engineering Department, The University of Texas at San Antonio, USA
推荐引用方式
GB/T 7714
Wei Jiang,Shan Liang,Like Dong,et al. Cross-domain cooperative deep stacking network for speech separation[C],2015.
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