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EXPLOITING SPECTRO-TEMPORAL STRUCTURES USING NMF FOR DNN-BASED SUPERVISED SPEECH SEPARATION
Shuai, Nie1; Shan, Liang1; Hao, Li2; XueLiang, Zhang2; ZhanLei, Yang1; WenJu, Liu1; LiKe, Dong3
2016
会议名称IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
会议录名称IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
页码469-473
会议日期2016
会议地点Shang Hai, China
出版者IEEE
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.
关键词Speech Separation Deep Neural Network Nonnegative Matrix Factorization Spectro-temporal Structures
收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11023
专题模式识别国家重点实验室_机器人视觉
作者单位1.National Laboratory of Patten Recognition, Institute of Automation, Chinese Academy of Sciences
2.College of Computer Science, Inner Mongolia University
3.Electric Power Research Institute of ShanXi Electric Power Company, China State Grid Corp
推荐引用方式
GB/T 7714
Shuai, Nie,Shan, Liang,Hao, Li,et al. EXPLOITING SPECTRO-TEMPORAL STRUCTURES USING NMF FOR DNN-BASED SUPERVISED SPEECH SEPARATION[C]:IEEE,2016:469-473.
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