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A RobustICA-based algorithmic system for blind separation of convolutive mixtures

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Abstract

In this paper, we propose a frequency-domain method employing robust independent component analysis (RICA) to address the multichannel Blind Source Separation (BSS) problem of convolutive speech mixtures in highly reverberant environments. We impose regularization processes to tackle the ill-conditioning problem of the covariance matrix and to mitigate the performance degradation. We evaluate the impact of several parameters on the performance of separation, e.g., windowing type and overlapping ratio of the frequency domain method. We then assess and compare different techniques to solve the frequency-domain permutation ambiguity. Furthermore, we develop an algorithm to separate the source signals in adverse conditions, i.e. high reverberation conditions when short observation signals are available. Finally, through extensive simulations and real-world experiments, we evaluate and demonstrate the superiority of the presented convolutive algorithmic system in comparison to other BSS algorithms, including recursive regularized ICA (RR-ICA) and independent vector analysis (IVA).

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References

  • Aichner, R., Buchner, H., Yan, F., & Kellermann, W. (2006). A real-time blind source separation scheme and its application to reverberant and noisy acoustic environments. Signal Processing, 86(6), 1260–1277.

    Article  MATH  Google Scholar 

  • Albataineh, Z., & Salem, F. (2013a). New Blind Multiuser Detection in DS-CDMA Using H-DE and ICA Algorithms, 4th International Conference on Intelligent Systems, Modelling and Simulation, Bangkok, Thailand, 2013, pp. 569–574

  • Albataineh, Z., & Salem, F. (2013b). New blind multiuser detection in DS-CDMA based on extension of efficient fast independent component analysis (EF-ICA), 4th International Conference on Intelligent Systems, Modelling and Simulation. Bangkok, Thailand, pp. 543–548

  • Albataineh, Z., & Salem, F. (2020). Two pairwise iterative schemes for high dimensional blind source separation. International Journal of Speech Technology.

  • Albataineh, Z. (2018). Blind Decoding of Massive MIMO Uplink Systems Based on the Higher Order Cumulants. Wireless Personal Communications, 103, 1835–1847.

    Article  Google Scholar 

  • Albataineh, Z. (2018). Robust blind channel estimation algorithm for linear STBC systems using fourth order cumulant matrices. Telecommunication Systems, 68, 573–582.

    Article  Google Scholar 

  • Albataineh, Z., Hayajneh, K., Salameh, H., Dang, C., & Dagmseh, A. (2020). Robust massive MIMO channel estimation for 5G networks using compressive sensing technique. AEU-International Journal of Electronics and Communications, 120, 153197.

    Article  Google Scholar 

  • Bataineh, Z., & Salem, F. (2014). RobustICA-Based algorithm for blind separation of convolutive mixtures. arXiv: 1408.0193

  • Bataineh, Z., & Salem, F. (2018). A convex Cauchy–Schwarz divergence measure for blind source separation. International Journal of Circuits, Systems and Signal Processing, 12, 94–104.

    Google Scholar 

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

    Article  Google Scholar 

  • Benzvi, D., & Shafir, A. (2018). An ICA Algorithm for Separation of Convolutive Mixture of Periodic Signals, 2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE), Eilat, Israel, 2018, pp. 1-5.

  • Cardoso, F. (1994a). On the performance of orthogonal source separation algorithms. In: Proc. EUSIPCO, pp. 776–779.

  • Cardoso, J.-F. (1999). High-order contrasts for independent component analysis. Neural Computation, 11(1), 157–192.

    Article  Google Scholar 

  • Chien, J.-T., & Hsieh, H.-L. (2013). Nonstationary source separation using sequential and variational Bayesian learning. IEEE Transactions on Neural Networks and Learning Systems, 24(5), 681–694.

    Article  MathSciNet  Google Scholar 

  • Cichocki, A., Zdunek, R., Amari, S., Hori, G., & Umeno, K. (2006). Blind signal separation method and system using modular and hierarchical-multilayer processing for blind multidimensional decomposition, identification, separation or extraction, Patent pending, No. 2006-124167, RIKEN, Japan.

  • Cichocki, A., & Amari, S. (2002). Adaptive blind signal and image processing. Chichester: Wiley.

    Book  Google Scholar 

  • Cichocki, A., Zdunek, R., & Amari, S.-I. (2009). Nonnegative matrix and tensor factorizations: applications to exploratory multi-way analysis and Blind Source Separation. Hoboken: Wiley.

    Book  Google Scholar 

  • Comon, P. (1994). Independent component analysis, a new concept. Signal Processing, 36(3), 287–314.

    Article  MATH  Google Scholar 

  • Comon, P., & Jutten, C. (Eds.). (2010). Handbook of blind source separation independent component analysis and applications. Oxford: Academic Press.

    Google Scholar 

  • Douglas, S. C., & Gupta, M. (2007). Scaled natural gradient algorithms for instantaneous and convolutive blind source separation. Proc. ICASSP, II, 637–640.

    Google Scholar 

  • Douglas, S. C., & Sun, X. (2002). Convolutive blind separation of speech mixtures using the natural gradient. Speech Communication, 39, 65–78.

    Article  MATH  Google Scholar 

  • Hyvarinen, A. (1999). Fast and robust fixed-point algorithm for independent component analysis. IEEE Transactions on Neural Network, 10(3), 626–634.

    Article  Google Scholar 

  • Hyvarinen, A., & Oja, E. (1997). A fast fixed-point algorithm for independent component analysis. Neural Computation, 9(7), 1483–1492.

    Article  Google Scholar 

  • Ikeshita, R., Ito, N., Nakatani, T., & Sawada, H. (2019). A unifying framework for blind source separation based on a joint diagonalizability constraint. 27th European Signal Processing Conference (EUSIPCO). A Coruna, Spain, 2019, 1–5.

  • Koldovsky, Z., & Tichavsky, P. (2008). Time-domain blind audio source separation using advanced component clustering and reconstruction. In: Proceedings of HSCMA, Trento, Italy, May 2008.

  • Lee, I., Kim, T., & Lee, T.-W. (2007). Independent vector analysis for convolutive blind speech separation. In: Blind speech eparation. Springer, New York.

  • Lin, C.-H., Chi, C.-Y., Chen, L., Miller, D. J., & Wang, Y. (2018). Detection of sources in non-negative blind source separation by minimum description length criterion. IEEE Transactions on Neural Networks and Learning Systems, 29(9), 4022–4037.

    Article  MathSciNet  Google Scholar 

  • Low, S. Y., Nordholm, S., & Togneri, R. (2004). Convolutive blind signal separation with post-processing. IEEE Transactions on Speech and Audio Processing, 12(5), 539.

    Article  Google Scholar 

  • Mitianoudis, N., & Davies, M. (2003). Audio source separation of convolutive mixtures. IEEE Transactions on Speech and Audio Processing, 11(5), 489–497.

    Article  Google Scholar 

  • Nesta, F., Svaizer, P., & Omologo, M. (2011). Convolutive BSS of short mixtures by ICA recursively regularized across frequencies. IEEE Transactions on Audio, Speech, and Language Processing, 19(3), 624–639.

    Article  Google Scholar 

  • Nest, F., Svaizer, P., & Omologo, M. (2011). Convolutive BSS of short mixtures by ICA recursively regularized across frequencies. IEEETrans. Audio, Speech, Lang. Process, 19(3), 624–639.

    Article  Google Scholar 

  • Nion, D., Mokios, K. N., Sidiropoulos, N. D., & Potamianos, A. (2010). Batch and adaptive PARAFAC-based blind separation of convolutive speech mixtures. IEEE Transactions on Audio, Speech and Language Processing, 18(6), 1193–1207.

    Article  Google Scholar 

  • Ollila, E. (2010). The deflation-based fastICA estimator: Statistical analysis revisited. IEEE Transactions on Signal Processing, 58(3), 1527–1541.

    Article  MathSciNet  MATH  Google Scholar 

  • Parra, L., & Spence, C. (2000). Convolutive blind separation of non-stationary sources. IEEE Transactions on Speech and Audio Processing, 8(3), 320–327.

    Article  MATH  Google Scholar 

  • Pearlmutter, B.A., & Parra, L.C. (1996). Maximum likelihood blind source separation: A context-sensitive generalization of ICA, Adv. Neural Inf. Process. Syst., pp. 613–619.

  • Pedersen, M. S., Larsen, J., Kjems, U., & Parra, L. C. (2007). A survey of convolutive blind source separation methods, in Springer Handbook of Speech Processing. New York: Springer.

    Google Scholar 

  • Pham, D.-T., Servi, C., egravere & Boumaraf, H. (2003). Blind separation of convolutive audio mixtures using nonstationarity. In: Proc. Int. Workshop Indep. Compon. Anal. Blind Signal Separation (ICA03), pp. 981–986 (2003).

  • Pham, D. T., & Garat, P. (1997). Blind separation of mixture of independent sources through a quasi-maximum likelihood approach. IEEE Transactions on Signal Processing, 45(7), 1712–1725.

    Article  MATH  Google Scholar 

  • Rahbar, K., & Reilly, J.-P. (2005). A frequency domain method for blind source separation of convolutive audio mixtures. IEEE Transactions on Speech and Audio Processing, 13(5), 832–844.

    Article  Google Scholar 

  • Saito, S., Oishi, K., & Furukawa, T. (2015). Convolutive blind source separation using an iterative least-squares algorithm for non-orthogonal approximate joint diagonalization. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(12), 2434–2448.

    Article  Google Scholar 

  • Saruwatari, H., Kawamura, T., Nishikawa, T., Lee, A., & Shikano, K. (2006). Blind source separation based on a fast-convergence algorithm combining ICA and beamforming. IEEE Transactions on Audio, Speech and Language Processing, 14(2), 666.

    Article  Google Scholar 

  • Sawada, H., Araki, S., & Makino, S. (2007). Frequency-domain blind source separation. In: Blind Speech Separation. Springer, September (2007).

  • Sawada, H., Araki, S., Mukai, R., & Makino, S. (2006). Blind extraction of dominant target sources using ICA and time frequency masking. IEEE Transactions on Audio, Speech and Language Processing, 14(6), 2165–2173.

    Article  Google Scholar 

  • Sawada, H., Mukai, R., Araki, S., & Makino, S. (2004). A robust and precise method for solving the permutation problem of frequency-domain blind source separation. IEEE Transactions on Speech and Audio Processing, 12(5), 530–538.

    Article  Google Scholar 

  • Serviegravere, C., & Pham, D.-T. (2006). Permutation correction in the frequency domain in blind separation of speech mixtures, EURASIP J. Appl. Signal Process., no. 1, pp. 1–16

  • Solvang, H. K., Nagahara, Y., Araki, S., Sawada, H., & Makino, S. (2009). Frequency-domain Pearson distribution approach for independent component analysis (FD-Pearson-ICA) in blind source separation. IEEE Transactions on Audio, Speech and Language Processing, 17(4), 639.

    Article  Google Scholar 

  • Sun, C., Yang, L., Chen, L., & Zhang, J. (2019). SVR based blind signal recovery for convolutive MIMO systems with high-order QAM signals. IEEE Access, 7, 23249–23260.

    Article  Google Scholar 

  • van der Veen, A.-J., & Paulraj, A. (1996). An analytical constant modulus algorithm. IEEE Transactions on Signal Processing, 44, 1136–1155.

    Article  Google Scholar 

  • Vasin, V. V. (2006). Some tendencies in the Tikhonov regularization of ill-posed problems. Journal of Inverse and Ill-posed Problems, 14(8), 813–840.

    Article  MathSciNet  MATH  Google Scholar 

  • Vincent, E., Araki, S., & Boll, P. (2009). The 2008 signal separation evaluation campaign: A community-based approach to large-scale evaluation. In: ICA 09: Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation, pp. 734–741, Berlin

  • Vincent, E., Fevotte, C., & Gribonval, R. (2006). Performance measurement in blind audio source separation. IEEE Transactions on Audio, Speech and Language Processing, 14(4), 1462–1469.

    Article  Google Scholar 

  • Wada, T.S., & Juang, B.-H. (2009). Acoustic echo cancellation based on independent component analysis and integrated residual echo enhancement, Proc. WASPAA, pp. 205–208 (2009).

  • Williamson, D. S., & Wang, D. (2017). Time-frequency masking in the complex domain for speech dereverberation and denoising. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(7), 1492–1501.

    Article  Google Scholar 

  • Yang, J., Guo, Y., Yang, Z., & Xie, S. (2019). Under-determined convolutive blind source separation combining density-based clustering and sparse reconstruction in time-frequency domain. IEEE Transactions on Circuits and Systems I: Regular Papers, 66(8), 3015–3027.

    Article  MathSciNet  Google Scholar 

  • Yoshioka, Nakatani, T., Miyoshi, T., Okuno, M., & Hiroshi, G. (2011). Blind separation and dereverberation of speech mixtures by joint optimization. IEEE Transactions on Audio, Speech and Language Processing, 19(1), 69.

    Article  Google Scholar 

  • Zarzoso, V., & Comon, P. (2010). Robust independent component analysis by iterative maximization of the kurtosis contrast with algebraic optimal step size. IEEE Transactions on Neural Networks, 21(2), 248–261.

    Article  Google Scholar 

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Authors and Affiliations

  1. Electronics Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan

    Zaid Albataineh

  2. Circuits, Systems, And Neural Networks (CSANN) Laboratory Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824-1226, USA

    Fathi M. Salem

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  1. Zaid Albataineh
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Albataineh, Z., Salem, F.M. A RobustICA-based algorithmic system for blind separation of convolutive mixtures. Int J Speech Technol 24, 701–713 (2021). https://doi.org/10.1007/s10772-021-09833-z

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