February  2013, 7(1): 283-290. doi: 10.3934/ipi.2013.7.283

Source extraction in audio via background learning

1. 

Department of Mathematics, Michigan State University, East Lanisng, MI 48824, United States

2. 

Department of Mathematics, Michigan State University, East Lansing, MI 48824

Received  July 2010 Revised  October 2010 Published  February 2013

Source extraction in audio is an important problem in the study of blind source separation (BSS) with many practical applications. It is a challenging problem when the foreground sources to be extracted are weak compared to the background sources. Traditional techniques often do not work in this setting. In this paper we propose a novel technique for extracting foreground sources. This is achieved by an interval of silence for the foreground sources. Using this silence interval one can learn the background information, allowing the removal or suppression of background sources. Very effective optimization schemes are proposed for the case of two sources and two mixtures.
Citation: Yang Wang, Zhengfang Zhou. Source extraction in audio via background learning. Inverse Problems and Imaging, 2013, 7 (1) : 283-290. doi: 10.3934/ipi.2013.7.283
References:
[1]

A. J. Bell and T. J. Sejnowski, An information-maximization approach to blind separation and blind deconvolution, Neural Computation, 7 (1995), 1129-1159.

[2]

J. F. Cardoso and A. Souloumiac, Blind beamforming for non Gaussian signals, IEE proceedings-f, 1993.

[3]

S. Choi, A. Cichocki, H. M. Park and S. Y. Lee, Blind source separation and independent component analysis: A review, Neural Information Processing-Letters and Reviews, 6 (2005), 1-57.

[4]

A. Cichocki and S. Amari, "Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications," Wiley, 2002.

[5]

A. Hyvärinen and E. Oja, Independent component analysis: Algorithms and applications, Neural Networks, 13 (2000), 411-430.

[6]

A. Jourjine, S. Rickard and O. Yilmaz, Blind separation of disjoint orthogonal signals: Demixing n sources from 2 mixtures, in "IEEE International Conference on Acoustics Speech and Signal Processing," Vol. 5, IEEE, (2000), 2985-2988.

[7]

J. Liu, J. Xin and Y. Qi, A dynamic algorithm for blind separation of convolutive sound mixtures, Neurocomputing, 72 (2008), 521-532.

[8]

Y. Wang and Q. Wu, Sparse PCA by iterative elimination algorithm, Adv. Comput. Math., 36 (2012), 137-151. doi: 10.1007/s10444-011-9186-3.

[9]

Y. Wang, O. Yilmaz and Z. Zhou, Phase aliasing correction for robust blind source separation using DUET, to apeear in Applied and Computational Harmonic Analysis.

[10]

M. Yu, W.-Y. Ma, J. Xin and S. Osher, Convexity and fast speech extraction by split Bregman method, preprint.

show all references

References:
[1]

A. J. Bell and T. J. Sejnowski, An information-maximization approach to blind separation and blind deconvolution, Neural Computation, 7 (1995), 1129-1159.

[2]

J. F. Cardoso and A. Souloumiac, Blind beamforming for non Gaussian signals, IEE proceedings-f, 1993.

[3]

S. Choi, A. Cichocki, H. M. Park and S. Y. Lee, Blind source separation and independent component analysis: A review, Neural Information Processing-Letters and Reviews, 6 (2005), 1-57.

[4]

A. Cichocki and S. Amari, "Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications," Wiley, 2002.

[5]

A. Hyvärinen and E. Oja, Independent component analysis: Algorithms and applications, Neural Networks, 13 (2000), 411-430.

[6]

A. Jourjine, S. Rickard and O. Yilmaz, Blind separation of disjoint orthogonal signals: Demixing n sources from 2 mixtures, in "IEEE International Conference on Acoustics Speech and Signal Processing," Vol. 5, IEEE, (2000), 2985-2988.

[7]

J. Liu, J. Xin and Y. Qi, A dynamic algorithm for blind separation of convolutive sound mixtures, Neurocomputing, 72 (2008), 521-532.

[8]

Y. Wang and Q. Wu, Sparse PCA by iterative elimination algorithm, Adv. Comput. Math., 36 (2012), 137-151. doi: 10.1007/s10444-011-9186-3.

[9]

Y. Wang, O. Yilmaz and Z. Zhou, Phase aliasing correction for robust blind source separation using DUET, to apeear in Applied and Computational Harmonic Analysis.

[10]

M. Yu, W.-Y. Ma, J. Xin and S. Osher, Convexity and fast speech extraction by split Bregman method, preprint.

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