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《电子学报(英文)》 2018-05
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Single-Channel Speech Separation Based on Non-negative Matrix Factorization and Factorial Conditional Random Field

LI Xu;TU Ming;WANG Xiaofei;WU Chao;FU Qiang;YAN Yonghong;Key Laboratory of Speech Acoustics and Content Understanding,Institute of Acoustics,Chinese Academy of Sciences;Signal Analysis Representation and Perception Laboratory,Arizona State University;Xinjiang Laboratory of Minority Speech and Language Information Processing;  
A new Non-negative matrix factorization(NMF) based algorithm is proposed for single-channel speech separation with a prior known speakers, which aims to better model the spectral structure and temporal continuity of speech signal. First, NMF and k-means clustering are employed to obtain multiple small dictionaries as well as a state sequence that describes the temporal dynamics between these dictionaries for each speaker.Then, a Factorial conditional random field(FCRF) model is trained using the state sequences and dictionaries to jointly model the temporal continuity of two speakers' mixed signal for separation. Experiments show that the proposed algorithm outperforms the baselines with respect to all metrics, for example sparse NMF(+1.12 dB SDR, +2.37 dB SIR, +0.40 dB SAR, +0.2 MOS), nonnegative factorial hidden Markov model(+2.04 dB SDR,+4.26 dB SIR, +0.62 dB SAR, +1.0 MOS) and standard NMF(+2.8 dB SDR, +5.08 dB SIR, +1.06 dB SAR, +1.2 MOS).
【Fund】: supported by the National Natural Science Foundation of China(No.11461141004 No.11590770 No.11590771 No.11590772 No.11590773 No.11590774);; the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA06030100 No.XDA06030500);; the National High Technology Research and Development Program of China(863 Program)(No.2015AA016306);; the Key Science and Technology Project of the Xinjiang Uygur Autonomous Region(No.201230118-3)
【CateGory Index】: TN912.34
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