Abstract
Blind deconvolution is an ill-posed problem. To solve such a problem, prior information, such as, the sparseness of the source (i.e., input) signal or channel impulse responses, is usually adopted. In speech deconvolution, the source signal is not naturally sparse. However, the direct impulse and early reflections of the impulse responses of an acoustic system can be considered as sparse. In this paper, we exploit the channel sparsity and present an algorithm for speech deconvolution, where the dynamic range of the convolutive speech is also used as the prior information. In this algorithm, the estimation of the impulse response and the source signal is achieved by alternating between two steps, namely, the \(\ell _1\) regularized least squares optimization and a proximal operation. As demonstrated in our experiments, the proposed method provides superior performance for deconvolution of a sparse acoustic system, as compared with two state-of-the-art methods.
Similar content being viewed by others
References
A. Adiga, C.S. Seelamantula, An alternating \(\ell _p-\ell _2\) projections algorithm (ALPA) for speech modeling using sparsity constraints, in Proceedings of IEEE International Conference on Digital Signal Processing (DSP) (2014), pp. 291–296
A. Ahmed, B. Recht, J. Romberg, Blind deconvolution using convex programming. IEEE Trans. Inf. Theory 60(3), 1711–1732 (2014)
A. Alinaghi, P.J. Jackson, Q. Liu, W. Wang, Joint mixing vector and binaural model based stereo source separation. IEEE/ACM Trans. Audio Speech Lang. Process. 22(9), 1434–1448 (2014)
J.B. Allen, D.A. Berkley, Image method for efficiently simulating small-room acoustics. J. Acoust. Soc. Am. 65(4), 943–950 (1979)
A. Benichoux, E. Vincent, R. Gribonval, A fundamental pitfall in blind deconvolution with sparse and shift-invariant priors, in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2013), pp. 26–31
J. Bolte, P.L. Combettes, J.C. Pesquet, Alternating proximal algorithm for blind image recovery, in: Proceedings of IEEE International Conference on Image Processing (ICIP) (2010), pp. 1673–1676
S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)
P. Campisi, K. Egiazarian, Blind Image Deconvolution: Theory and Applications (CRC press, Boca Raton, 2007)
R. Chai, G. Naik, T.N. Nguyen, S. Ling, Y. Tran, A. Craig, H. Nguyen, Driver fatigue classification with independent component by entropy rate bound minimization analysis in an EEG-based system. J. Biomed. Health Inform. (2016). doi:10.1109/JBHI.2016.2532354
Y. Chi, Guaranteed blind sparse spikes deconvolution via lifting and convex optimization. IEEE J. Sel. Top. Signal Process. 10(4), 782–794 (2016)
S. Choudhary, U. Mitra, Fundamental limits of blind deconvolution part I: ambiguity kernel. ArXiv preprint arXiv:1411.3810 (2014)
S. Choudhary, U. Mitra, Fundamental limits of blind deconvolution part II: sparsity-ambiguity trade-offs. ArXiv preprint arXiv:1503.03184 (2015)
E. Chouzenoux, J.C. Pesquet, A. Repetti, Variable metric forward-backward algorithm for minimizing the sum of a differentiable function and a convex function. J. Optim. Theory Appl. 162(1), 107–132 (2014)
E. Chouzenoux, J.C. Pesquet, A. Repetti, A block coordinate variable metric forward-backward algorithm. J. Glob. Optim. 66(3), 457–485 (2016)
P.L. Combettes, J.C. Pesquet, Proximal splitting methods in signal processing (2009), pp. 1–25. http://arxiv.org/abs/0912.3522
D.L. Donoho, On minimum entropy deconvolution. in Applied Time-Series Analysis II (Academic Press, 1981), pp. 569–609
M. Grant, S. Boyd, M. Grant, S. Boyd, V. Blondel, S. Boyd, H. Kimura, CVX: Matlab software for disciplined convex programming, version 2.1. (2014). http://cvxr.com/cvx/
Y. Guo, S. Huang, Y. Li, G.R. Naik, Edge effect elimination in single-mixture blind source separation. Circuits Syst. Signal Process. 32(5), 2317–2334 (2013)
Y. Guo, G.R. Naik, H. Nguyen, Single channel blind source separation based local mean decomposition for biomedical applications, in Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6812–6815 (2013)
S. Haykin, Blind Deconvolution (Prentice Hall, Englewood Cliffs, 1994)
K.F. Kaaresen, Deconvolution of sparse spike trains by iterated window maximization. IEEE Trans. Signal Process. 45(5), 1173–1183 (1997)
K.F. Kaaresen, T. Taxt, Multichannel blind deconvolution of seismic signals. Geophysics 63(6), 2093–2107 (1998)
C. Kelley, Iterative methods for linear and nonlinear equations. SIAM Front. Appl. Math. 16, 11–30 (1995)
S.J. Kim, K. Koh, M. Lustig, S. Boyd, D. Gorinevsky, An interior-point method for large-scale \(\ell _1\)-regularized least squares. IEEE J. Sel. Top. Signal Process. 1(4), 606–617 (2007)
D. Krishnan, T. Tay, R. Fergus, Blind deconvolution using a normalized sparsity measure, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011), pp. 233–240
X. Li, S. Ling, T. Strohmer, K. Wei, Rapid, robust, and reliable blind deconvolution via nonconvex optimization. ArXiv preprint arXiv:1606.04933 (2016)
H. Liu, S. Liu, T. Huang, Z. Zhang, Y. Hu, T. Zhang, Infrared spectrum blind deconvolution algorithm via learned dictionaries and sparse representation. Appl. Opt. 55(10), 2813–2818 (2016)
Y. Luo, W. Wang, J.A. Chambers, S. Lambotharan, I. Proudler, Exploitation of source nonstationarity in underdetermined blind source separation with advanced clustering techniques. IEEE Trans. Signal Process. 54(6), 2198–2212 (2006)
G. Naik, A. Al-Timemy, H. Nguyen, Transradial amputee gesture classification using an optimal number of sEMG sensors: an approach using ICA clustering. IEEE Trans. Neural Syst. Rehabil. Eng. 24(8), 837–846 (2016)
G. Naik, S. Selvan, H. Nguyen, Single-channel EMG classification with ensemble-empirical-mode-decomposition-based ICA for diagnosing neuromuscular disorders. IEEE Trans. Neural Syst. Rehabil. Eng. 24(7), 734–743 (2016)
G.R. Naik, Enhancement of the ill-conditioned original recordings using novel ICA technique. Int. J. Electron. 99(7), 899–906 (2012)
G.R. Naik, D.K. Kumar, Estimation of independent and dependent components of non-invasive EMG using fast ICA: validation in recognising complex gestures. Comput. Methods Biomech. Biomed. Eng. 14(12), 1105–1111 (2011)
N. Parikh, S. Boyd, Proximal algorithms. Found. Trends Optim. 1(3), 127–239 (2014)
G. Pendharkar, G.R. Naik, H.T. Nguyen, Using blind source separation on accelerometry data to analyze and distinguish the toe walking gait from normal gait in ITW children. Biomed. Signal Process. Control 13, 41–49 (2014)
A. Repetti, E. Chouzenoux, J.C. Pesquet, A preconditioned forward–backward approach with application to large-scale nonconvex spectral unmixing problems, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2014), pp. 1498–1502
A. Repetti, M.Q. Pham, L. Duval, E. Chouzenoux, J.C. Pesquet, Euclid in a taxicab: sparse blind deconvolution with smoothed regularization. IEEE Signal Process. Lett. 22(5), 539–543 (2015)
I. Selesnick, Sparse deconvolution (an MM algorithm). http://cnx.org/content/m44991/ (2012)
R. Tibshirani, Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodological) 58(1), 267–288 (1996)
E. Vincent, R. Gribonval, C. Févotte, Performance measurement in blind audio source separation. IEEE Trans. Audio Speech Lang. Process. 14(4), 1462–1469 (2006)
L. Wang, Y. Chi, Blind deconvolution from multiple sparse inputs. IEEE Signal Process. Lett. 23(10), 1384–1388 (2016)
W. Wang, M.G. Jafari, S. Sanei, J.A. Chambers, Blind separation of convolutive mixtures of cyclostationary signals. Int. J. Adapt. Control Signal Process. 18(3), 279–298 (2004)
W. Wang, S. Sanei, J.A. Chambers, Penalty function-based joint diagonalization approach for convolutive blind separation of nonstationary sources. IEEE Trans. Signal Process. 53(5), 1654–1669 (2005)
T. Xu, W. Wang, W. Dai, Sparse coding with adaptive dictionary learning for underdetermined blind speech separation. Speech Commun. 55(3), 432–450 (2013)
Y. Yu, W. Wang, P. Han, Localization based stereo speech source separation using probabilistic time-frequency masking and deep neural networks. EURASIP J. Audio Speech Music Process. 2016(1), 1–18 (2016)
H. Zhang, D. Wipf, Y. Zhang, Multi-observation blind deconvolution with an adaptive sparse prior. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1628–1643 (2014)
Acknowledgements
The work was conducted when J. Guan was visiting the University of Surrey, and supported in part by International Exchange and Cooperation Foundation of Shenzhen City, China (No. GJHZ20150312114149569). W. Wang was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) Grant Number EP/K014307 and the MOD University Defence Research Collaboration in Signal Processing. The authors thank the anonymous reviewers for their helpful suggestions and Dr. Mark Barnard for proofreading the revised manuscript.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Guan, J., Wang, X., Wang, W. et al. Sparse Blind Speech Deconvolution with Dynamic Range Regularization and Indicator Function. Circuits Syst Signal Process 36, 4145–4160 (2017). https://doi.org/10.1007/s00034-017-0505-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00034-017-0505-x