|Table of Contents|

[1] Peng Tianliang, Chen Yang,. A two-stage frequency-domain blind source separation methodfor underdetermined instantaneous mixtures [J]. Journal of Southeast University (English Edition), 2016, 32 (2): 135-140. [doi:10.3969/j.issn.1003-7985.2016.02.001]
Copy

A two-stage frequency-domain blind source separation methodfor underdetermined instantaneous mixtures()
一种基于频域的欠定瞬时混合2步盲分离法
Share:

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
32
Issue:
2016 2
Page:
135-140
Research Field:
Computer Science and Engineering
Publishing date:
2016-06-20

Info

Title:
A two-stage frequency-domain blind source separation methodfor underdetermined instantaneous mixtures
一种基于频域的欠定瞬时混合2步盲分离法
Author(s):
Peng Tianliang Chen Yang
School of Information Science and Engineering, Southeast University, Nanjing 210096, China
彭天亮 陈阳
东南大学信息科学与工程学院, 南京210096
Keywords:
inverse truncated mixing matrix under-determined blind source separation(UBSS) frequency domain matrix completion
截断混合矩阵求逆 欠定盲源分离 频域 矩阵填充
PACS:
TP391
DOI:
10.3969/j.issn.1003-7985.2016.02.001
Abstract:
In order to decrease the probability of missing some data points or noises being added in the inverse truncated mixing matrix(ITMM)algorithm, a two-stage frequency-domain method is proposed for blind source separation of underdetermined instantaneous mixtures. The separation process is decomposed into two steps of ITMM and matrix completion in the view that there are many soft-sparse(not very sparse)sources. First, the mixing matrix is estimated and the sources are recovered by the traditional ITMM algorithm in the frequency domain. Then, in order to retrieve the missing data and remove noises, the matrix completion technique is applied to each preliminary estimated source by the traditional ITMM algorithm in the frequency domain. Simulations show that, compared with the traditional ITMM algorithms, the proposed two-stage algorithm has better separation performances. In addition, the time consumption problem is considered. The proposed algorithm outperforms the traditional ITMM algorithm at a cost of no more than one-fourth extra time consumption.
为了减少传统的截断混合矩阵求逆(ITMM)算法在个别时频点会丢失数据或者产生噪声信号的概率, 提出了一种基于频域的2步欠定瞬时盲分离算法.由于现实中存在大量软稀疏(稀疏度不是很大)混合信号, 将分离过程分解为ITMM和矩阵补偿2个步骤.首先估计出混合矩阵和利用经典的ITMM算法对混合信号进行初步恢复, 然后对初步估计的信号时频矩阵进行矩阵补偿处理, 从而达到修补丢失数据和去除多余数据(去噪)的效果.实验仿真验证了所提出的2步分离法相对于传统的ITMM算法能够得到更好的分离效果.此外, 对算法的时耗问题进行了研究, 相对于传统的ITMM算法, 所提算法的时耗增加不到四分之一, 却能够得到更好的分离效果.

References:

[1] Cobos M, Lopez J J. Maximum a posteriori binary mask estimation for underdetermined source separation using smoothed posteriors [J]. IEEE Transactions on Audio, Speech, and Language Processing, 2012, 20(7): 2059-2064. DOI:10.1109/tasl.2012.2195654.
[2] Leglaive S, Badeau R, Richard G. Multichannel audio source separation with probabilistic reverberation modeling[C]//IEEE Workshop on Applications of Signal Processing to Audio and Acoustics(WASPAA). New York, USA, 2015: 1-5.
[3] Peng Tianliang, Chen Yang, Liu Zengli. A time-frequency domain blind source separation method for underdetermined instantaneous mixtures [J]. Circuits Systems & Signal Processing, 2015, 34(12): 3883-3895.
[4] Peng Tianliang, Chen Yang. Inverse truncated mixing matrix(ITMM)algorithm application to underdetermined convolutive blind speech sources separation [C]//IEEE International Conference on Signal Processing, Communications and Computing. Ningbo, China, 2015: 771-776.
[5] Yilmaz O, Rickard S. Blind separation of speech mixtures via time-frequency masking [J]. IEEE Transactions on Signal Processing, 2004, 52(7):1830-1847.
[6] Sawada H, Araki S, Makino S. Underdetermined convolutive blind source separation via frequency bin-wise clustering and permutation alignment [J].IEEE Transactions on Audio, Speech, and Language Processing, 2011, 19(3): 516-527. DOI:10.1109/tasl.2010.2051355.
[7] Linh-Trung N, Belouchrani A, Abed-Meraim K, et al. Separating more sources than sensors using time-frequency distributions [J]. EURASIP Journal on Applied Signal Processing, 2005, 2005: 2828-2847. DOI:10.1155/asp.2005.2828.
[8] Belouchrani A, Abed-Meraim K, Cardoso J F, et al. A blind source separation technique using second-order statistics [J]. IEEE Transactions on Signal Processing, 1997, 45(2): 434-444. DOI:10.1109/78.554307.
[9] Cai J F, Candès E J, Shen Z. A singular value thresholding algorithm for matrix completion [J]. SIAM Journal on Optimization, 2010, 20(4):1956-1982. DOI:10.1137/080738970.
[10] Candès E J, Recht B. Exact matrix completion via convex optimization [J]. Foundations of Computational Mathematics, 2009, 9(6): 717-772.
[11] Vincent E, Sawada H, Bofill P, et al. First stereo audio source separation evaluation campaign: Data, algorithms and results [M]//Independent Component Analysis and Signal Separation. Berlin: Springer, 2007: 552-559.
[12] Vincent E, Gribonval R, Févotte C. Performance measurement in blind audio source separation [J]. IEEE Transactions on Audio, Speech, and Language Processing, 2006, 14(4): 1462-1469. DOI:10.1109/tsa.2005.858005.

Memo

Memo:
Biographies: Peng Tianliang(1984—), male, graduate; Chen Yang(corresponding author), male, doctor, professor, cheny@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No.60872074).
Citation: Peng Tianliang, Chen Yang. A two-stage frequency-domain blind source separation method for underdetermined instantaneous mixtures[J].Journal of Southeast University(English Edition), 2016, 32(2):135-140.doi:10.3969/j.issn.1003-7985.2016.02.001.
Last Update: 2016-06-20