Abstract
This paper proposes a direction of arrival (DOA) estimation method for an acoustic source using linear sensor arrays on the basis of generalized regression neural network (GRNN). The real and imaginary parts of the received data of linear sensor arrays in the frequency domain are vectorized and spliced into a one-dimensional sequence as the input feature. The application of this method is studied in three scenarios on noiseless, noisy, and hybrid training sets. Simulations show that the GRNN algorithm has higher accuracy at high SNRs than the support vector machine (SVM), convolutional neural network (CNN) and multiple signal classification (MUSIC) methods, and only the GRNN method can estimate the DOA effectively at low SNRs. According to the different accuracy requirements in practical applications, this paper also provides the selection rules for an appropriate training set for the GRNN method. Therefore, the GRNN method can achieve effective the DOA estimation in different SNR environments of many scenarios.
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References
Alam, F., Usman, M., & Alkhammash, H. I. (2021). Improved direction-of-arrival estimation of an acoustic source using support vector regression and signals correlation. Sensors, 21, 2692. https://doi.org/10.3390/s21082692
Bartlett, M. S. (1937). Properties of sufficiency and statistical tests. Proceedings of the Royal Society of London, 160(901), 268–282. https://doi.org/10.1007/978-1-4612-0919-5_8
Besson, O., & Stoica, P. (1999). A fast and robust algorithm for DOA estimation of a spatially dispersed source. Digital Signal Processing, 9(4), 267–279. https://doi.org/10.1006/dspr.1999.0345
Candes, E.J. (2006). Compressive campling. Proceedings of the International Congress of Mathematicians, Madrid, Spain, pp. 1433–1452.
Capon, J. (1969). High-resolution frequency-wavenumber spectrum analysis. Proceedings of the IEEE, 57(8), 1408–1481. https://doi.org/10.1109/PROC.1969.7278
Dibiase, J. H. (2000). A high-accuracy, low-latency technique for talker localization in reverberant environments using microphone arrays. European Journal of Biochemistry, 216(1), 281–291. https://doi.org/10.1111/j.1432-1033.1993.tb18143.x
Drucker, H., Burges, C. J. C., & Kaufman, L. (1997). Support vector regression machines. Advances in Neural Information Processing Systems, 28(7), 779–784.
Ferguson, E.L., Williams, S.B., & Jin, C.T. (2018). Sound source localization in a multipath environment using convolutional neural networks. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, (pp. 2386–2390).
Fukushima, K., Miyake, S., & Ito, T. (1983). Neocognitron: A neural network model for a mechanism of visual pattern recognition. IEEE Transactions on Systems, Man and Cybernetics. https://doi.org/10.1007/978-3-642-46466-9_18
He, M., Zheng, Z., & Wang, W. (2021). Pattern synthesis for uniform linear array using genetic algorithm and artificial neural network. Multidim Syst Sign Process.
Huang, Y., Benesty, J., & Elko, G. W. (2001). Real-time passive source localization: A practical linear-correction least-squares approach. IEEE Transactions on Speech and Audio Processing, 9(8), 943–956. https://doi.org/10.1109/89.966097
Ishi, C. T., Chatot, O., & Ishiguro, H. (2009). Evaluation of a MUSIC-based real-time sound localization of multiple sound sources in real noisy environments. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009, 2027–2032. https://doi.org/10.1109/IROS.2009.5354309
Jin, Y., Huang, J., & Zhang, L. (2009). A fast high-resolution method for bearing estimation in shallow ocean. Multidimensional Systems and Signal Processing, 20(4), 397–406. https://doi.org/10.1007/s11045-008-0070-3
Knapp, C., & Carter, G. (1976). The generalized correlation method for estimation of time delay. IEEE Transactions on Acoustics, Speech, and Signal Processing, 24(4), 320–327. https://doi.org/10.1109/TASSP.1976.1162830
Liang, J., & Liu, D. (2010). Joint estimation of source number and DOA using simulated annealing algorithm. Digital Signal Processing, 20(3), 887–899. https://doi.org/10.1016/j.dsp.2009.08.007
Parzen, E. (1962). On estimation of a probability density function and mode. The Annals of Mathematical Statistics, 33(3), 1065–1076. https://doi.org/10.1214/aoms/1177704472
Reed, F., Feintuch, P., & Bershad, N. (1981). Time delay estimation using the LMS adaptive filter-Static behavior. IEEE Transactions on Acoustics, Speech, and Signal Processing, 29(3), 561–571. https://doi.org/10.1109/TASSP.1981.1163614
Roy, R., & Kailath, T. (1989). SPRIT-estimation of signal parameters via rotational invariance techniques. IEEE Transactions on Acoustics, Speech, and Signal Processing, 37(7), 984–995. https://doi.org/10.1109/29.32276
Salvati, D., Drioli, C., & Foresti, G. L. (2016). A weighted MVDR beamformer based on SVM learning for sound source localization. Pattern Recognition Letters, 84, 15–21. https://doi.org/10.1016/j.patrec.2016.07.003
Schmidt, R. (1986). Multiple emitter location and signal parameter estimation. IEEE Transactions on Antennas and Propagation, 34(3), 276–280. https://doi.org/10.1109/TAP.1986.1143830
Specht, D. F. (1991). A general regression neural network. IEEE Transactions on Neural Networks, 2(6), 568–576. https://doi.org/10.1109/72.97934
Takashima, R., Takiguchi, T., & Ariki Y. (2010). HMM-based separation of acoustic transfer function for single-channel sound source localization. In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, (pp. 2830–2833).
Wax, M., & Kailath, T. (1983). Optimum localization of multiple sources by passive arrays. IEEE Transactions on Acoustics, Speech, and Signal Processing, 31(5), 1210–1217. https://doi.org/10.1109/TASSP.1983.1164183
Xu, X. L., & Buckley, K. M. (1992). Bias analysis of the MUSIC location estimator. IEEE Transactions on Signal Processing, 40(10), 2559–2569. https://doi.org/10.1109/78.157296
Zheng, Z., & Li, G. (2013). Fast DOA estimation of incoherently distributed sources by novel propagator. Multidimensional Systems and Signal Processing, 24(3), 573–581. https://doi.org/10.1007/s11045-012-0185-4
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This work was supported in part by the National Natural Science Foundation of China under Grants No. 11974286, 61971353 and 11904274.
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Yao, Q., Wang, Y., Yang, Y. et al. DOA estimation using GRNN for acoustic sensor arrays. Multidim Syst Sign Process 34, 575–594 (2023). https://doi.org/10.1007/s11045-023-00877-9
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DOI: https://doi.org/10.1007/s11045-023-00877-9