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Noise Time-Domain Signal Reconstruction of Passenger Head Position Considering Compressed Sensing and Multi-source Data Fusion

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Abstract

Sound field reconstruction technology is used to provide accurate primary reference signals for active noise control systems by reconstructing the interior sound field. Traditionally, time-domain noise signal-based reconstruction modeling has certain deficiencies, such as large data volume, noise reconstruction model complexity and considerable time consumption. Hence, a novel Signal compression optimisation-based BP network for passenger head position signal reconstruction (CBHSR) algorithm is proposed. Based on compressed sensing, the proposed algorithm converts raw multi-source signals into the compressed domain to implement compressed sampling. The signal reconstruction model is created by regarding the optimal fitness value as the initial weight and the threshold of the signal reconstruction BP network, and training with the compressed multi-source data. The recovery compression signal method realizes the time-domain signal reconstruction of the passenger head position. The effectiveness of the proposed CBHSR algorithm is validated using noise signal sources collected from a vehicle. Compared with the reconstruction model of the BP algorithm, the proposed algorithm is superior in reconstruction accuracy and time consumption.

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Data availability

The raw test data are derived from the school-enterprise joint program to achieve active noise control. Thus, the raw data would remain confidential and would not be shared for the purpose to protect the potential commercial value.

References

  1. M.M. Abozahhad, A.I. Hussein, A.M. Mohamed, Compression of ECG signal based on compressive sensing and the extraction of significant features. Int. J. Commun. Netw. Syst. Sci. 8(5), 97–117 (2015)

    Google Scholar 

  2. A.C.M. Austin, M.J. Neve, Efficient field reconstruction using compressive sensing. IEEE Trans. Antennas Propag. 99, 1–1 (2018)

    Google Scholar 

  3. E. Bjarnason, Analysis of the filtered-X LMS algorithm. IEEE Trans. Speech Audio Process 3(6), 504–514 (1993)

    Article  Google Scholar 

  4. J. Bleiholder, F. Naumann, Data fusion. ACM Comput. 41, 1–41 (2008)

    Google Scholar 

  5. E.J. Candès, The restricted isometry property and its implications for compressed sensing. C. R. Math. 346(9), 589–592 (2008)

    Article  MathSciNet  Google Scholar 

  6. E.J. Candes, J. Romberg, T. Tao, Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information (IEEE Press, New York, 2006).

    MATH  Google Scholar 

  7. E.J. Candes, M.B. Wakin, An introduction to compressive sensing. IEEE Signal Process. Mag. 25(2), 21–30 (2008)

    Article  Google Scholar 

  8. X.W. Chen, X. Lin, Big data deep learning: challenges and perspectives. IEEE Access 2, 514–525 (2014)

    Article  Google Scholar 

  9. F. Cui, Study of traffic flow prediction based on BP neural network, in Intelligent Systems and Applications (ISA), pp. 1–4 (2010).

  10. J. Cusido, L. Romeral, J.A. Ortega et al., Fault detection in induction machines using power spectral density in wavelet decomposition. IEEE Trans. Ind. Electron. 55, 633–643 (2008)

    Article  Google Scholar 

  11. Y. Da, G. Xiurun, An improved PSO-based ANN with simulated annealing technique. Neurocomputing 63, 527–533 (2005)

    Article  Google Scholar 

  12. D.L. Donoho, Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)

    Article  MathSciNet  Google Scholar 

  13. M.F. Duarte, M.A. Davenport, D. Takhar et al., Single-pixel imaging via compressive sampling. IEEE Signal Process. Mag. 25(2), 83–91 (2008)

    Article  Google Scholar 

  14. S.J. Elliott, J. Cheer, L. Bhan et al., A wavenumber approach to analysing the active control of plane waves with arrays of secondary sources. J. Sound Vib. 419, 405–419 (2018)

    Article  Google Scholar 

  15. Z. Feng, Z. Lin, W. Fang, et al. Analog circuit fault fusion diagnosis method based on support vector machine, in International Symposium on Neural Networks, (Springer, Berlin, Heidelberg, 2009), pp. 225–234

  16. Z. Feng, M.J. Zuo, Vibration signal models for fault diagnosis of planetary gearboxes. J. Sound Vib. 331(22), 4919–4939 (2012)

    Article  Google Scholar 

  17. V.D.B. Frans, A.P. Engelbrecht, A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 225–239 (2004)

    Article  Google Scholar 

  18. R. Hecht-Nielsen, Theory of the back-propagation neural network, in IJCNN, IEEE, vol. 1, pp. 593–605 (1989)

  19. N.E. Huang, Z. Shen, S.R. Long et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Math. Phys. Eng. Sci. 454(1971), 903–995 (1998)

    Article  MathSciNet  Google Scholar 

  20. J. Kennedy, Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance, in Congress on Evolutionary Computation IEEE (1999).

  21. G.N. Lilis, D. Angelosante, G.B. Giannakis, Sound Field Reproduction Using the Lasso (IEEE Press, New York, 2010).

    Book  Google Scholar 

  22. J. MacQueen, Some methods for classification and analysis of multivariate observations, in Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1(14), pp. 281–297 (1967).

  23. T.M. Quan, T. Nguyen-Duc, W.K. Jeong, Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss. IEEE Trans. Med. lmaging 37(6), 1488–1497 (2018)

    Article  Google Scholar 

  24. M. Rani, S.B. Dhok, R.B. Deshmukh, A systematic review of compressive sensing: concepts implementations and applications. IEEE Access 6(99), 4875–4894 (2018)

    Article  Google Scholar 

  25. D.E. Rumelhart, Learning Internal Representations by Error Propagation. Parallel Distributed Processing. Explorations in the Microstructure of Cognition (1986).

  26. F. Sahin, M.C. Yavuz, Z. Arnavut, Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization. Parallel Comput. 33, 124–143 (2007)

    Article  MathSciNet  Google Scholar 

  27. H.D. Shao, H.K. Jiang, H.Z. Zhang et al., Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing. Mech. Syst. Signal Process. 100, 743–765 (2018)

    Article  Google Scholar 

  28. S.A. Abouel-Seoud, Active control analysis of passenger vehicle interior noise produced from tire/road interaction. Int. J. Veh. Noise Vib. 12(2), 138–161 (2016)

    Article  Google Scholar 

  29. D. Slonim, P. Tamayo, J. Mesirov et al., Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. 96, 2907–2912 (1999)

    Article  Google Scholar 

  30. R. Souza, R. Frayne, A hybrid frequency-domain/image -domain deep network for magnetic resonance image reconstruction, in 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) IEEE, pp. 257—264 (2019).

  31. J. Tropp, A.C. Gilbert, Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)

    Article  MathSciNet  Google Scholar 

  32. J. Wang, X. Liao, P. Zheng et al., Classification of Chinese herbal medicine by laser induced breakdown spectroscopy with principal component analysis and artificial neural network. Anal. Lett. 1, 1–12 (2017)

    Google Scholar 

  33. M. Wang, C.B. Xiao, Z.H. Ning et al., Neural networks for compressed sensing based on information geometry. Circuits Syst. Signal Process. 38, 569–589 (2019)

    Article  MathSciNet  Google Scholar 

  34. X. Wang, T. Wang, L. Su et al., Adaptive active vehicle interior noise control algorithm based on nonlinear signal reconstruction. Circuits Syst. Signal Process. 39, 5226–5246 (2020)

    Article  Google Scholar 

  35. Y.S. Wang, C.M. Lee, D.G. Kim et al., Sound-quality prediction for nonstationary vehicle interior noise based on wavelet pre-processing neural network model. J. Sound Vib. 299, 933–947 (2007)

    Article  Google Scholar 

  36. L. Xu, J. Zhang, Y. Yan, A wavelet-based multi-sensor data fusion algorithm. IEEE Trans. Instrum. Meas. 53, 1539–1544 (2004)

    Article  Google Scholar 

  37. D. Yang, X. Wang, Y. Wang et al., A multi-source fusion algorithm for high-accuracy signal reconstruction of vehicle interior noise on passenger ear-sides. Appl. Acoust. 148, 75–85 (2019)

    Article  Google Scholar 

  38. P.K.D.V. Yarlagadda, A neural network system for the prediction of process parameters in pressure die casting. J. Mater. Process. Technol. 89, 583–590 (1999)

    Article  Google Scholar 

  39. X.H. Zhang, Q.S. Lian, Y.C. Yang et al., A deep unrolling network inspired by total variation for compressed sensing MRI. Digital Signal Process. 107, 102856 (2020)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, No. 333, Longteng Road, Songjiang District, Shanghai, 201620, People’s Republic of China

    D. P. Yang, X. L. Wang & Y. S. Wang

  2. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130025, People’s Republic of China

    D. P. Yang, D. F. Song & X. H. Zeng

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  1. D. P. Yang
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  2. X. L. Wang
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  3. Y. S. Wang
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  4. D. F. Song
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  5. X. H. Zeng
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Correspondence to X. L. Wang.

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Yang, D.P., Wang, X.L., Wang, Y.S. et al. Noise Time-Domain Signal Reconstruction of Passenger Head Position Considering Compressed Sensing and Multi-source Data Fusion. Circuits Syst Signal Process 40, 5533–5552 (2021). https://doi.org/10.1007/s00034-021-01731-8

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  • DOI: https://doi.org/10.1007/s00034-021-01731-8

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