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Quantum-Source Independent Component Analysis and Related Statistical Blind Qubit Uncoupling Methods Read chapter Read first chapter

2014 | OriginalPaper | Chapter

5. Frequency Domain Blind Source Separation Based on Independent Vector Analysis with a Multivariate Generalized Gaussian Source Prior

Authors : Yanfeng Liang, Syed Mohsen Naqvi, Wenwu Wang, Jonathon A. Chambers

Published in: Blind Source Separation

Publisher: Springer Berlin Heidelberg

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Abstract

Independent vector analysis (IVA) is designed for retaining the dependency contained in each source vector, while removing the dependency between different source vectors during the source separation process. It can theoretically avoid the permutation problem inherent to independent component analysis (ICA). The dependency in each source vector is maintained by adopting a multivariate source prior instead of a univariate source prior. In this chapter, a multivariate generalized Gaussian distribution is proposed to be the source prior, which can exploit the energy correlation within each source vector. It can preserve the dependency between different frequency bins better to achieve an improved separation performance, and is suitable for the whole family of IVA algorithms. Experimental results on real speech signals confirm the advantage of adopting the new source prior on three types of IVA algorithms.

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previous chapter Subband-Based Blind Source Separation and Permutation Alignment
next chapter Sparse Component Analysis: A General Framework for Linear and Nonlinear Blind Source Separation and Mixture Identification
Literature
1.
Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. Wiley, New York (2003)
2.
Comon, P., Jutten, C.: Handbook of Blind Source Separation: Independent Component Analysis and Applications. Academic Press, Oxford (2009)
3.
Cherry, C.: Some experiments on the recognition of speech, with one and with two ears. J. Acoust. Soc. Am. 25, 975–979 (1953)CrossRef
4.
Cherry, C., Taylor, W.: Some further experiments upon the recognition of speech, with one and with two ears. J. Acoust. Soc. Am. 26, 554–559 (1954)CrossRef
5.
Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, New York (2001)
6.
Parra, L., Spence, C.: Convolutive blind separation of non-stationary sources. IEEE Trans. Speech Audio Process. 8, 320–327 (2000)CrossRef
7.
Pedersen, M.S., Larsen, J., Kjems, U., Parra, L.C.: A survey of convolutive blind source separation methods. In: Handbook on Speech Processing and Speech Communication. Springer, New York (2007)
8.
Kim, T., Lee, I., Lee, T.-W.: Independent vector analysis: definition and algorithms. In: Fortieth Asilomar Conference on Signals, Systems and Computers 2006. Asilomar, USA (2006)
9.
Kim, T., Attias, H., Lee, S., Lee, T.: Blind source separation exploiting higher-order frequency dependencies. IEEE Trans. Audio Speech Lang. Process. 15, 70–79 (2007)CrossRef
10.
Kim, T.: Real-time independent vector analysis for convolutive blind source separation. IEEE Trans. Circuits Syst. 57, 1431–1438 (2010)CrossRef
11.
Lee, I., Kim, T., Lee, T.-W.: Fast fixed-point independent vector analysis algorithms for convolutive blind source separation. Signal Process. 87, 1859–1871 (2007)CrossRefMATH
12.
Ono, N.: Stable and fast update rules for independent vector analysis based on auxiliary function technique. In: 2011 IEEE WASPAA. New Paltz, USA (2011)
13.
Liang, Y., Naqvi, S.M., Chambers, J.: Adaptive step size indepndent vector analysis for blind source separation. In: 17th International Conference on Digital Signal Processing. Corfu, Greece (2011)
14.
Liang, Y., Naqvi, S.M., Chambers, J.: Audio video based fast fixed-point independent vector analysis for multisource separation in a room environment. EURASIP J. Adv. Signal Process. 2012, 183 (2012)CrossRef
15.
Masnadi-Shirazi, A., Zhang, W., Rao, B.D.: Glimpsing IVA: A framework for overcomplete/complete/undercomplete convolutive source separation. IEEE Trans. Audio Speech Lang. Process. 18, 1841–1855 (2010)
16.
Ono, T., Ono, N., Sagayama, S.: User-guided indpendent vector analysis with source activity tuning. In: ICASSP 2012. Kyoto, Japan (2012)
17.
Lee, I., Jang, G.-J., Lee, T.-W.: Independent vector analysis using densities represented by chain-like overlapped cliques in graphical models for separation of convolutedly mixed signals. Electron. Lett. 45, 710–711 (2009)CrossRef
18.
Choi, C.H., Chang, W., Lee, S.Y.: Blind source separation of speech and music signals using harmonic frequency dependent independent vector analysis. Electron. Lett. 48, 124–125 (2012)CrossRef
19.
Lee, I., Hao, J., Lee, T.W.: Adaptive independent vector analysis for the separation of convoluted mixtures using EM algorithm. In: ICASSP 2008. USA, Las Vegas (2008)
20.
Hao, J., Lee, I., Lee, T.W.: Independent vector analysis for source separation using a mixture of Gaussian prior. Neural Comput. 22, 1646–1673 (2010)CrossRefMATHMathSciNet
21.
Anderson, M., Adali, T., Li, X.-L.: Joint blind source separation with multivariate Gaussian model: algorithms and performance analysis. IEEE Trans. Signal Process. 60, 1672–1682 (2012)CrossRefMathSciNet
22.
Hyvärinen, A.: Independent component analysis: recent advances. Philos. Transact. A Math. Phys. Eng. Sci. 371(1984), 1–19 (2012)CrossRef
23.
Garofolo, J.S., et al.: TIMIT acoustic-phonetic continuous speech corpus. Linguistic Data Consortium, Philadelphia (1993)
24.
Lee, I., Lee, T.W.: On the assumption of spherical symmetry and sparseness for the frequency-domain speech model. IEEE Trans. Audio Speech Lang. Process. 15, 1521–1528 (2007)
25.
Ono, N., Miyabe, S.: Auxiliary-function-based independent component analysis for super-Gaussian source. In: LVA/IVA 2010. St. Malo, France (2010)
26.
Vincent, E., Fevotte, C., Gribonval, R.: Performance measurement in blind audio source separation. IEEE Trans. Audio Speech Lang. Process. 14, 1462–1469 (2006)CrossRef
Metadata
Title
Frequency Domain Blind Source Separation Based on Independent Vector Analysis with a Multivariate Generalized Gaussian Source Prior
Authors
Yanfeng Liang
Syed Mohsen Naqvi
Wenwu Wang
Jonathon A. Chambers
Copyright Year
2014
Publisher
Springer Berlin Heidelberg
Book
Blind Source Separation

Print ISBN: 978-3-642-55015-7

Electronic ISBN: 978-3-642-55016-4

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-642-55016-4_5