Journal
JOURNAL OF SOUND AND VIBRATION
Volume 457, Issue -, Pages 67-91Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jsv.2019.05.037
Keywords
Underdetermined blind source separation; Single source point; Synchrosqueezing transform; Density peaks clustering; l(1)-norm decomposition
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Funding
- Project of the National Natural Science Foundation of China [51775407]
- National Natural Science Foundation of China [61633001]
- General Project of Joint Preresearch Fund for Equipment of Ministry of Education [6141A02022121]
- Fundamental Research Funds for the Central Universities
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It has been challenging to correctly separate sources from their mixtures with large noise and unknown source number in underdetermined cases. To address this problem, a novel underdetermined blind source separation (UBSS) method is proposed using synchrosqueezing transform (SST) and improved density peaks clustering (DPC). First, SST is employed to reassign time-frequency (TF) coefficients of short time Fourier transform (STFT) of mixed signals from their original position to the center of gravity along frequency direction, contributing to sparser TF representation than STFT. Second, a single source point (SSP) identification method is proposed by directly searching the identical normalized TF vectors, which considers linear representation relations among TF vectors and therefore can identify SSPs more accurately in noisy cases. Third, DPC is improved to automatically identify source number, with which the mixing matrix can be estimated. After that, sources are finally recovered using li-norm decomposition method. The effectiveness of the proposed method is validated with some numerical studies and experimental studies. The results show that the proposed method can estimate source number correctly, and recover sources accurately even in noisy cases. (C) 2019 Elsevier Ltd. All rights reserved.
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