Blind separation of acoustic sources in reverberant environments

In many real-world scenarios, different source signals are mixed in a dispersive environment, and there is significant interest to separate the sources solely on the basis of the observed mixtures, without prior knowledge of the sources or the mixing system. A well-known example is a cocktail party situation, where many people are speaking at the same time in a reverberant room, and a listener has to separate out the signal of interest. Other applications of blind source separation include the ecg analysis of mother and fetus, fMRI data analysis, image restoration, and digital communications over multipath channels. In this project, we aim at finding technical solutions for the separation of sounds in acoustic reverberant environments solely on the basis of observed microphone signals. The project has been funded by the German Research Foundation (DFG).

Project Members

Radoslaw Mazur, Tiemin Mei, Alfred Mertins

A CUDA Implementation of Independent Component Analysis in the Time-Frequency Domain:

Publications

2013

  • Nov 2013
    Mazur, R., Jungmann, J. O., and Mertins, A.: A new clustering approach for solving the permutation problem in convolutive blind source separation. Proc. Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, USA, Oct. 2013
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  • Okt 2013
    Mazur, R., Jungmann, J. O., and Mertins, A.: Optimized Dyadic Sorting for Solving the Permutation Ambiguity in Acoustic Blind Source Separation. Proc. European Signal Processing Conference, Marrakech, Maroc, Sept 2013
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  • Mär 2013
    Mazur, R., Jungmann, J. O., and Mertins, A.: A Curtosis Based Criterion for Solving the Permutation Ambiguity in Convolutive Blind Source Separation. Proc. AIA-DAGA 2013 Conference on Acoustics, Merano, Italy, pp. 187-188, Mar. 2013
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2012

  • Mär 2012
    Mazur, R., Jungmann, J. O., and Mertins, A.: Optimized Gradient Calculation for Room Impulse Response Reshaping Algorithm Based on p-Norm Optimization. Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing, Kyoto, Japan, pp. 185-188, Mar. 2012
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2011

  • Jul 2011
    Mazur, R. and Mertins, A.: A CUDA Implementation of Independent Component Analysis in the Time-Frequency Domain. Proc. European Signal Processing Conference, Barcelona, Spain, pp. 512-514, Aug 2011
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  • Mai 2011
    Mazur, R. and Mertins, A.: A Sparsity Based Criterion for Solving the Permutation Ambiguity in Convolutive Blind Source Separation. Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing, Prague, Czech Republic, pp. 1996-1999, May 2011
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2010

  • Sep 2010
    Mazur, R. and Mertins, A.: A Method for Filter Equalization in Convolutive Blind Source Separation. Proc. 9th Int. Conf. on Latent Variable Analysis and Signal Separation, St. Malo, France, Sept. 2010
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  • Aug 2010
    Mazur, R. and Mertins, A.: Improving the Robustness of the Correlation Approach for Solving the Permutation Problem in the Convolutive Blind Source Separation. Proc. International Workshop on Acoustic Echo and Noise control (IWAENC), Tel Aviv, Israel, Aug. 2010
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2009

  • Aug 2009
    Mazur, R. and Mertins, A.: Simplified Formulation of a Depermutation Criterion in Convolutive Blind Source Separation. Proc. European Signal Processing Conference, Glasgow, Scotland, pp. 1467-1470, Aug 2009
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  • Apr 2009
    Mazur, R. and Mertins, A.: Using the scaling ambiguity for filter shortening in convolutive blind source separation. Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing, Taipei, Taiwan, pp. 1709-1712, April 2009
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  • Mär 2009
    Mazur, R. and Mertins, A.: A Method for Filter Shaping in Convolutive Blind Source Separation. Independent Component Analysis and Signal Separation (ICA2009), Springer, LNCS, vol. 5441, pp. 282-289, 2009
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  • Jan 2009
    Mazur, R. and Mertins, A.: An Approach for Solving the Permutation Problem of Convolutive Blind Source Separation based on Statistical Signal Models. IEEE Trans. Audio, Speech, and Language Processing, vol. 17, no. 1, pp. 117--126, Jan. 2009
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2008

  • Nov 2008
    Mei, T. and Mertins, A.: Convolutive Blind Source Separation Based on Disjointness Maximization of Subband Signals. IEEE Signal Processing Letters, vol. 15, pp. 725-728, 2008
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  • Okt 2008
    Mazur, R. and Mertins, A.: On Separation Performance Enhancement in Convolutive Blind Source Separation. Proc. Asilomar Conference on Signals, Systems, and Computers, pp. 1718-1721, Oct. 2008
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  • Jul 2008
    Mei, T., Mertins, A., and Yin, F.: On the Generalization of Blind Source Separation Algorithms from Instantaneous to Convolutive Mixtures. Proc. IEEE sensor array and multi-channel signal processing workshop (SAM2008), Darmstadt, pp. 482-486, July 2008
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  • Jan 2008
    Mei, T., Mertins, A., Yin, F., Xi, J., and Chicharo, J. F.: Blind Source Separation for Convolutive Mixtures Based on the Joint Diagonalization of Power Spectral Density Matrices. Signal Processing, vol. 88, no. 8, pp. 1990--2007, 2008
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2007

  • Sep 2007
    Mazur, R. and Mertins, A.: Solving the Permutation Problem in Convolutive Blind Source Separation. Independent Component Analysis and Signal Separation, Davies, M. E., James, C. J., Abdallah, S. A., and Plumbley, M. D. (Ed.), Springer, LNCS, vol. 4666, pp. 512-519, 2007
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2006

  • Nov 2006
    Mei, T., Xi, J., Yin, F., Mertins, A., and Chicharo, J. F.: Blind Source Separation Based on Time-domain Optimizations of a Frequency-domain Independence Criterion: IEEE Trans. Audio Speech and Language Processing, Nov. 2006
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  • Sep 2006
    Mazur, R. and Mertins, A.: Reducing reverberation effects in convolutive blind source separation: Proc. European Signal Processing Conference, Florence Italy, Sept. 2006
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2005

  • Aug 2005
    Mei, T., Yin, F., Xi, J., and Chicharo, a. . A. M. J. F.: A half-frequency domain approach for convolutive blind source separation based on Kullback-Leibler divergence. Proc. of the Eighth Int. Symposium on Signal Processing and Its Applications, Sydney, Australia, vol. 1, pp. 25-28, Aug. 2005
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