CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
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
Conference NameIEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Source PublicationIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pages469-473
Conference Date2016
Conference PlaceShang Hai, China
PublisherIEEE
AbstractThe 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.
KeywordSpeech Separation Deep Neural Network Nonnegative Matrix Factorization Spectro-temporal Structures
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11023
Collection模式识别国家重点实验室_机器人视觉
Affiliation1.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
Recommended Citation
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.
Files in This Item: Download All
File Name/Size DocType Version Access License
ShuaiNie2016.pdf(408KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Shuai, Nie]'s Articles
[Shan, Liang]'s Articles
[Hao, Li]'s Articles
Baidu academic
Similar articles in Baidu academic
[Shuai, Nie]'s Articles
[Shan, Liang]'s Articles
[Hao, Li]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Shuai, Nie]'s Articles
[Shan, Liang]'s Articles
[Hao, Li]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: ShuaiNie2016.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.