Abstract
This chapter presents an overview of criteria and algorithms for the blind separation of linearly mixed acoustic signals. Particular attention is paid to the underlying statistical formulations of various approaches to the convolutive blind signal separation task, and comparisons to other blind inverse problems are made. Several algorithms are described, including a novel algorithm that largely maintains the spectral content of the original mixtures in the extracted source signals. Numerical experiments are also provided to explore the behaviors of the algorithms in real-world blind signal separation tasks.
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References
C. Cherry, “Some experiments on the recognition of speech with one and two ears,” J. Acoust. Soc. Amer., vol. 25, pp. 975–981, 1953.
W. Yost, Fundamentals of Hearing, 3rd ed., Academic, 1994.
S. Van Gerven and D. Van Compernolle, “Signal separation by symmetric adaptive decorrelation: stability, convergence, and uniqueness,” IEEE. Trans. Signal Processing, vol. 43, pp. 1602–1612, July 1995.
E. Weinstein, M. Feder, and A. Oppenheim, “Multi-channel signal separation by decorrelation,” IEEE. Trans. Speech Audio Processing, vol. 1, pp. 405–413, Oct. 1993.
P. Comon, “Independent component analysis: A new concept?”, Signal Processing, vol. 36, no. 3, pp. 287–314, Apr. 1994.
C. Jutten and J. Herault, “Blind separation of sources, Part I: An adaptive algorithm based on neuromimetic architecture,” Signal Processing, vol. 24, pp. 1–10, 1991.
S. Haykin, ed., Unsupervised Adaptive Filtering, Vol. I: Blind Source Separation, Wiley, 2000.
J.-F. Cardoso, “Blind signal separation: Statistical principles,” Proc. IEEE, vol. 90, pp. 2009–2026, 1998.
S. Douglas, “Blind Signal Separation and Blind Deconvolution,” in Handbook of Neural Networks for Signal Processing, (J.-N. Hwang and Y.-H. Hu, eds. ), CRC Press, 2001.
A. Bell and T. Sejnowski, “An information maximization approach to blind separation and blind deconvolution,” Neural Comput., vol. 7, pp. 1129–1159, 1995.
S. Amari, S. Douglas, A. Cichocki, and H. Yang, “Novel on-line adaptive learning algorithms for blind deconvolution using the natural gradient approach,” in Proc. 11th IFAC Symp. Syst. Ident., Kitakyushu City, Japan, pp. 1057–1062, July 1997.
R. Lambert and A. Bell, “Blind separation of multiple speakers in a multipath environment,” in Proc. IEEE Conf. Acoust., Speech, Signal Processing (ICASSP-97), Munich, Germany, pp. 423–426, Apr. 1997.
T.-W. Lee et al.,“Combining time-delayed decorrelation and ICA: towards solving the cocktail party problem,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP-98),Seattle WA, USA, pp. 1089–1092, May 1998.
Y. Inouye and T. Sato, “Iterative algorithms based on multistage criteria for multichannel blind deconvolution,” IEEE Trans. Signal Processing, vol. 47, no. 6, pp. 1759–1764, 1999.
J. Tugnait, “Identification and deconvolution of multichannel linear non-Gaussian processes using higher-order statistics and inverse filter criteria,” IEEE Trans. Signal Processing, vol. 45, pp. 658–672, Mar. 1997.
S. Haykin, ed., Blind Deconvolution, Wiley, 1994.
S. Haykin, ed., Unsupervised Adaptive Filtering, Vol. II: Blind Deconvolution, Wiley, 2000.
T. Cover and J. Thomas, Elements of Information Theory, Wiley, 1991.
S. Amari, A. Cichocki, and H. Yang, “A new learning algorithm for blind signal separation,” in Proc. Sys. Adv. Neural Inform., pp. 757–763, MIT Press, 1996.
S. Amari, T.-P. Chen, and A. Cichocki, “Stability analysis of learning algorithms for blind source separation,” Neural Networks, vol. 10, no. 8, pp. 13451351, Nov. 1997.
W. Davenport, Jr., “A study of speech probability distributions,” Tech. Rep. 148, MIT Research Laboratory of Electronics, Cambridge, MA, 1950.
S. Douglas, A. Cichocki, and S. Amari, “Multichannel blind separation and de-convolution of sources with arbitrary distributions,” in Proc. IEEE Int. Workshop Neural Networks Signal Processing, Amelia Island FL, USA, pp. 436–445, Sept. 1997.
D.-T. Pham and P. Garat, “Blind separation of mixture of independent sources through a quasi-maximum likelihood approach,” IEEE Trans. Signal Processing, vol. 45, pp. 1712–1725, July 1997.
D.-T. Pham, “Mutual information approach to blind separation of stationary sources,” in Proc. Workshop Indep. Compon. Anal. Signal Sep., Aussois, France, pp. 215–220, Jan. 1999.
C. Simon et al.,“Separation of a class of convolutive mixtures: a contrast function approach,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP-97),Phoenix AZ, USA, pp. 1429–1432, Apr. 1997.
J. Tugnait, “On blind separation of convolutive mixtures of independent linear signals in unknown additive noise,” IEEE Trans. Signal Processing, vol. 46, pp. 3117–3123, Nov. 1998.
L. Molgedey and H. Schuster, “Separation of a mixture of independent signals using time delayed correlations,” Phys. Rev. Lett., vol. 72, pp. 3634–3637, June 1994.
L. Parra and C. Spence, “Convolutive blind separation of non-stationary sources,” IEEE Trans. Speech Audio Processing, vol. 8, pp. 320–327, May 2000.
J. Shynk, “Frequency-domain and multirate adaptive filtering,” IEEE Signal Processing Mag., vol. 9, pp. 14–37, Jan. 1992.
K. Torkkola, “Blind Separation of Delayed and Convolved Sources,” in Unsupervised Adaptive Filtering, Vol. I: Blind Source Separation, (S. Haykin, ed.), pp. 321–375, Wiley, 2000.
S. Douglas and S Amari, “Natural Gradient Adaptation,” in Unsupervised Adaptive Filtering, Vol. I: Blind Source Separation, (S. Haykin, ed.), pp. 1361, Wiley, 2000.
S. Amari, S. Douglas, A. Cichocki, and H. Yang, “Multichannel blind deconvolution and equalization using the natural gradient,” in Proc. Signal Processing Adv. Wireless Commun., Paris, France, pp. 101–104, Apr. 1997.
S. Amari, T.-P. Chen, and A. Cichocki, “Nonholonomic orthogonal learning algorithms for blind source separation,” Neural Comput., vol. 12, pp. 14631484, 2000.
P. Regalia and P. Loubaton, “Rational subspace estimation using adaptive loss-less filters,” IEEE Trans. Signal Processing, vol. 40, pp. 2392–2405, Oct. 1992.
S. Douglas, S. Amari, and S.-Y. Kung, “Adaptive paraunitary filter banks for spatio-temporal principal and minor subspace analysis,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP-99), Phoenix AZ, USA, pp. 1089–1092, Mar. 1999.
X. Sun and S. Douglas, “Multichannel blind deconvolution of arbitrary signals: Adaptive algorithms and stability analyses,” in Proc. 34th Asilomar Conf. Signals, Syst., Comput., Pacific Grove CA, USA, pp. 1412–1416, Oct. 2000.
The Bobs, Boy around the corner,“ from Songs For Tomorrow Morning, [audio recording], Rhino Records, 1988.
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Douglas, S.C. (2001). Blind Separation of Acoustic Signals. In: Brandstein, M., Ward, D. (eds) Microphone Arrays. Digital Signal Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04619-7_16
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DOI: https://doi.org/10.1007/978-3-662-04619-7_16
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