Distributed-Robust MVDR Beamforming With Energy-Efficient Topology Control in Wireless Acoustic Sensor Networks
Authors: 
De Hu, Qintuya Si, Weiwei ZhangAuthors Info & Claims
De Hu, Qintuya Si, Weiwei ZhangAuthors Info & ClaimsPages 15658 - 15672
Abstract
We are often surrounded by intelligent devices with one or more acoustic sensors, which constitute a wireless acoustic sensor network (WASN) and can be exploited for various audio/speech processing tasks. As the captured audio signals are inevitably corrupted by ambient noises, signal enhancement is vital in WASNs. To this end, this paper proposes a distributed-robust and energy-efficient MVDR beamformer (BF) for WASNs. Specifically, a distributed MVDR BF is first derived by recursively updating the inverse of the noise correlation matrix, which requires fewer data transmission without sacrificing performance. Then, its robust version is further designed to alleviate the adverse effects of parameter mismatches. Finally, an energy-efficient network topology control (EENTC) is carried out to reduce the energy consumption, by optimizing the weight matrix of the distributed averaging process. Since the proposed EENTC strategy involves non-convex programming, we transform it into a convex one and solve it via the Dinkelbach algorithm. Unlike the centralized BFs, the proposed method works without an additional central processor. Moreover, it is robust against parameter mismatch during beamforming and can reduce a large amount of data transmission. Simulation and real-world experimental results confirm the validity of the proposed method.
References
[1]
L. Turchet, G. Fazekas, M. Lagrange, H. S. Ghadikolaei, and C. Fischione, “The Internet of audio things: State of the art, vision, and challenges,” IEEE Internet Things J., vol. 7, no. 10, pp. 10233–10249, Oct. 2020.
[2]
J. Zhang, G. Zhang, and L. Dai, “Frequency-invariant sensor selection for MVDR beamforming in wireless acoustic sensor networks,” IEEE Trans. Wireless Commun., vol. 21, no. 12, pp. 10648–10661, Dec. 2022.
[3]
Z. Lin, M. Lin, B. Champagne, W. Zhu, and N. Al-Dhahir, “Secrecy-energy efficient hybrid beamforming for satellite-terrestrial integrated networks,” IEEE Trans. Commun., vol. 69, no. 9, pp. 6345–6360, Sep. 2021.
[4]
Z. Lin et al., “Pain without gain: Destructive beamforming from a malicious RIS perspective in IoT networks,” IEEE Internet Things J., vol. 11, no. 5, pp. 7619–7629, Mar. 2024.
[5]
J. A. Belloch, J. M. Badia, F. D. Igual, and M. Cobos, “Practical considerations for acoustic source localization in the IoT era: Platforms, energy efficiency, and performance,” IEEE Internet Things J., vol. 6, no. 3, pp. 5068–5079, Jun. 2019.
[6]
P. Aarabi and S. Zaky, “Robust sound localization using multi-source audiovisual information fusion,” Inf. Fusion, vol. 2, no. 3, pp. 209–223, Sep. 2001.
[7]
Q. Zhang, W. Xu, W. Zhang, J. Feng, and Z. Chen, “Multi-hypothesis square-root cubature Kalman particle filter for speaker tracking in noisy and reverberant environments,” IEEE/ACM Trans. Audio, Speech, Language Process., vol. 28, no. 1, pp. 1183–1197, Dec. 2020.
[8]
X. Shen, D. Shi, and W.-S. Gan, “A hybrid approach to combine wireless and earcup microphones for ANC headphones with error separation module,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), May 2022, pp. 8702–8706.
[9]
R. Chang, Z. Chen, and F. Yin, “Robust distributed noise suppression in acoustic sensor networks,” IEEE Sensors J., vol. 22, no. 18, pp. 18151–18161, Sep. 2022.
[10]
R. Van Rompaey and M. Moonen, “Distributed adaptive signal estimation in wireless sensor networks with partial prior knowledge of the desired sources steering matrix,” IEEE Trans. Signal Inf. Process. Netw., vol. 7, pp. 478–492, 2021.
[11]
R. Wang, Z. Chen, and F. Yin, “Active sampling rate calibration method for acoustic sensor networks,” IEEE/ACM Trans. Audio, Speech, Language Process., vol. 28, no. 1, pp. 3095–3107, Dec. 2020.
[12]
A. Plinge, F. Jacob, R. Haeb-Umbach, and G. Fink, “Acoustic microphone geometry calibration,” IEEE Signal Process. Mag., vol. 33, no. 4, pp. 14–29, Nov. 2016.
[13]
D. Hu, H. Zhang, F. Bao, and R. Wang, “Distributed sampling rate offset estimation over acoustic sensor networks based on asynchronous network Newton optimization,” IEEE/ACM Trans. Audio, Speech, Language Process., vol. 31, no. 1, pp. 301–312, Sep. 2023.
[14]
D. Hu, Z. Chen, and F. Yin, “Passive geometry calibration for microphone arrays based on distributed damped Newton optimization,” IEEE/ACM Trans. Audio, Speech, Language Process., vol. 29, no. 1, pp. 118–131, Oct. 2021.
[15]
D. Hu, Z. Chen, and F. Yin, “Geometry calibration for acoustic transceiver networks based on network Newton distributed optimization,” IEEE/ACM Trans. Audio, Speech, Language, Process., vol. 29, no. 1, pp. 1023–1032, Nov. 2021.
[16]
A. Hassani, J. Plata-Chaves, M. H. Bahari, M. Moonen, and A. Bertrand, “Multi-task wireless sensor network for joint distributed node-specific signal enhancement, LCMV beamforming and DOA estimation,” IEEE J. Sel. Topics Signal Process., vol. 11, no. 3, pp. 518–533, Apr. 2017.
[17]
A. Hassani, A. Bertrand, and M. Moonen, “Distributed node-specific direction-of-arrival estimation in wireless acoustic sensor networks,” in Proc. 21st Eur. Signal Process. Conf., Sep. 2013, pp. 1–5.
[18]
Y. Zeng and R. C. Hendriks, “Distributed delay and sum beamformer for speech enhancement in wireless sensor networks via randomized gossip,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), Mar. 2012, pp. 4037–4040.
[19]
Y. Zeng and R. C. Hendriks, “Distributed delay and sum beamformer for speech enhancement via randomized gossip,” IEEE/ACM Trans. Audio, Speech, Language Process., vol. 22, no. 1, pp. 260–273, Jan. 2014.
[20]
R. Heusdens, G. Zhang, R. Hendriks, Y. Zeng, and W. B. Kleijn, “Distributed MVDR beamforming for (wireless) microphone networks using message passing,” in Proc. Int. Workshop Acoustic Signal Enhancement, Sep. 2012, pp. 1–4.
[21]
M. O’Connor and W. B. Kleijn, “Diffusion-based distributed MVDR beamformer,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), Florence, Italy, May 2014, pp. 810–814.
[22]
A. Bertrand and M. Moonen, “Distributed LCMV beamforming in a wireless sensor network with single-channel per-node signal transmission,” IEEE Trans. Signal Process., vol. 61, no. 13, pp. 3447–3459, Jul. 2013.
[23]
Y. Zeng and R. C. Hendriks, “Distributed estimation of the inverse of the correlation matrix for privacy preserving beamforming,” Signal Process., vol. 107, pp. 109–122, Feb. 2015.
[24]
T. Sherson, W. B. Kleijn, and R. Heusdens, “A distributed algorithm for robust LCMV beamforming,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), Mar. 2016, pp. 101–105.
[25]
S. A. Vorobyov, A. B. Gershman, and Z.-Q. Luo, “Robust adaptive beamforming using worst-case performance optimization: A solution to the signal mismatch problem,” IEEE Trans. Signal Process., vol. 51, no. 2, pp. 313–324, Feb. 2003.
[26]
J. Xu, G. Liao, S. Zhu, and L. Huang, “Response vector constrained robust LCMV beamforming based on semidefinite programming,” IEEE Trans. Signal Process., vol. 63, no. 21, pp. 5720–5732, Nov. 2015.
[27]
X. Zhang, Y. Li, N. Ge, and J. Lu, “Robust minimum variance beamforming under distributional uncertainty,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), Apr. 2015, pp. 2514–2518.
[28]
B. D. Carlson, “Covariance matrix estimation errors and diagonal loading in adaptive arrays,” IEEE Trans. Aerosp. Electron. Syst., vols. AEP-24, no. 4, pp. 397–401, Jul. 1988.
[29]
J. Li, P. Stoica, and Z. Wang, “On robust Capon beamforming and diagonal loading,” IEEE Trans. Signal Process., vol. 51, no. 7, pp. 1702–1715, Jul. 2003.
[30]
A. I. Koutrouvelis, T. W. Sherson, R. Heusdens, and R. C. Hendriks, “A low-cost robust distributed linearly constrained beamformer for wireless acoustic sensor networks with arbitrary topology,” IEEE/ACM Trans. Audio, Speech, Language Process., vol. 26, no. 8, pp. 1434–1448, Aug. 2018.
[31]
Q. Wang, S. Guo, and K. C. Yiu, “Distributed acoustic beamforming with blockchain protection,” IEEE Trans. Ind. Informat., vol. 16, no. 11, pp. 7126–7135, Nov. 2020.
[32]
S. Joshi and S. Boyd, “Sensor selection via convex optimization,” IEEE Trans. Signal Process., vol. 57, no. 2, pp. 451–462, Feb. 2009.
[33]
A. Bertrand and M. Moonen, “Efficient sensor subset selection and link failure response for linear MMSE signal estimation in wireless sensor networks,” in Proc. 18th Eur. Signal Process. Conf., Aug. 2010, pp. 1092–1096.
[34]
J. Szurley, A. Bertrand, M. Moonen, P. Ruckebusch, and I. Moerman, “Energy aware greedy subset selection for speech enhancement in wireless acoustic sensor networks,” in Proc. 20th Eur. Signal Process. Conf. (EUSIPCO), Bucharest, Romania, Aug. 2012, pp. 789–793.
[35]
J. Zhang, S. P. Chepuri, R. C. Hendriks, and R. Heusdens, “Microphone subset selection for MVDR beamformer based noise reduction,” IEEE/ACM Trans. Audio, Speech, Language Process., vol. 26, no. 3, pp. 550–563, Mar. 2018.
[36]
J. Zhang, R. Heusdens, and R. C. Hendriks, “Rate-distributed spatial filtering based noise reduction in wireless acoustic sensor networks,” IEEE/ACM Trans. Audio, Speech, Language Process., vol. 26, no. 11, pp. 2015–2026, Nov. 2018.
[37]
J. Zhang, R. Tao, J. Du, and L.-R. Dai, “Energy-efficient sparsity-driven speech enhancement in wireless acoustic sensor networks,” IEEE/ACM Trans. Audio, Speech, Language Process., vol. 31, no. 1, pp. 215–228, Dec. 2023.
[38]
D. Hu, Q. Si, R. Liu, and F. Bao, “Distributed sensor selection for speech enhancement with acoustic sensor networks,” IEEE/ACM Trans. Audio, Speech, Language Process., vol. 31, no. 1, pp. 985–999, Oct. 2023.
[39]
J. Zhang, A. I. Koutrouvelis, R. Heusdens, and R. C. Hendriks, “Distributed rate-constrained LCMV beamforming,” IEEE Signal Process. Lett., vol. 26, no. 5, pp. 675–679, May 2019.
[40]
S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, “Randomized gossip algorithms,” IEEE Trans. Inf. Theory, vol. 52, no. 6, pp. 2508–2530, Jun. 2006.
[41]
B. D. Van Veen and K. M. Buckley, “Beamforming: A versatile approach to spatial filtering,” IEEE ASSP Mag., vol. 5, no. 2, pp. 4–24, Apr. 1988.
[42]
Z. Lin et al., “Refracting RIS-aided hybrid satellite-terrestrial relay networks: Joint beamforming design and optimization,” IEEE Trans. Aerosp. Electron. Syst., vol. 58, no. 4, pp. 3717–3724, Aug. 2022.
[43]
Z. Lin, M. Lin, T. de Cola, J.-B. Wang, W.-P. Zhu, and J. Cheng, “Supporting IoT with rate-splitting multiple access in satellite and aerial-integrated networks,” IEEE Internet Things J., vol. 8, no. 14, pp. 11123–11134, Jul. 2021.
[44]
J. Zhang, “Power optimized and power constrained randomized gossip approaches for wireless sensor networks,” IEEE Wireless Commun. Lett., vol. 10, no. 2, pp. 241–245, Feb. 2021.
[45]
S. Markovich-Golan, S. Gannot, and W. Kellermann, “Performance analysis of the covariance-whitening and the covariance-subtraction methods for estimating the relative transfer function,” in Proc. 26th Eur. Signal Process. Conf., Sep. 2018, pp. 2499–2503.
[46]
J. Zhang, R. Heusdens, and R. C. Hendriks, “Relative acoustic transfer function estimation in wireless acoustic sensor networks,” IEEE/ACM Trans. Audio, Speech, Language Process., vol. 27, no. 10, pp. 1507–1519, Oct. 2019.
[47]
X. Dang, W. Ma, E. A. P. Habets, and H. Zhu, “TDOA-based robust sound source localization with sparse regularization in wireless acoustic sensor networks,” IEEE/ACM Trans. Audio, Speech, Language Process., vol. 30, no. 1, pp. 1108–1123, Sep. 2022.
[48]
D. Colquhoun and A. G. Hawkes, “A Q-matrix cookbook,” in Single-Channel Recording. Boston, MA, USA: Springer, 1995, pp. 589–633.
[49]
M.-S. Chen and R. Liou, “Recursive MVDR for direction finding using circular arrays,” in Proc. 10th Int. Symp. Antenna Technol. Appl. Electromagn. URSI Conf., Jul. 2004, pp. 1–4.
[50]
L. Xiao and S. Boyd, “Fast linear iterations for distributed averaging,” in Proc. 42nd IEEE Int. Conf. Decis. Control, vol. 5, Dec. 2003, pp. 4997–5002.
[51]
Q. Wang, M. Hempstead, and W. Yang, “A realistic power consumption model for wireless sensor network devices,” in Proc. 3rd IEEE SECON, vol. 1, Sep. 2006, pp. 286–295.
[52]
A. I. Barros, J. B. G. Frenk, S. Schaible, and S. Zhang, “A new algorithm for generalized fractional programs,” Math. Program., vol. 72, no. 2, pp. 147–175, Feb. 1996.
[53]
Y. Yu, X. Bu, K. Yang, Z. Wu, and Z. Han, “Green large-scale fog computing resource allocation using joint Benders decomposition, Dinkelbach algorithm, ADMM, and branch-and-bound,” IEEE Internet Things J., vol. 6, no. 3, pp. 4106–4117, Jun. 2019.
[54]
M. Grant, S. Boyd, and Y. Ye. (2008). CVX: MATLAB Software for Disciplined Convex Programming. CVX Research. [Online]. Available: https://cvxr.com/cvx
[55]
J. F. Sturm, “Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones,” Optim. Methods Softw., vol. 11, nos. 1–4, pp. 625–653, Jan. 1999.
[56]
Q. Shi, C. Peng, W. Xu, M. Hong, and Y. Cai, “Energy efficiency optimization for MISO SWIPT systems with zero-forcing beamforming,” IEEE Trans. Signal Process., vol. 64, no. 4, pp. 842–854, Feb. 2016.
[57]
J. S. Garofolo, “DARPA TIMIT acoustic-phonetic speech database,” Nat. Inst. Standards Thechnol., vol. 15, pp. 29–50, Sep. 1988.
[58]
E. A. Lehmann, A. M. Johansson, and S. Nordholm, “Reverberation-time prediction method for room impulse responses simulated with the image-source model,” in Proc. IEEE Workshop Appl. Signal Process. Audio Acoust., Oct. 2007, pp. 159–162.
[59]
R. O. Saber and R. M. Murray, “Consensus protocols for networks of dynamic agents,” in Proc. Amer. Control Conf., vol. 2, Jun. 2003, pp. 951–956.
[60]
A. W. Rix, J. G. Beerends, M. P. Hollier, and A. P. Hekstra, “Perceptual evaluation of speech quality (PESQ)—A new method for speech quality assessment of telephone networks and codecs,” in IEEE Int. Conf. Acoust., Speech, Signal Proc., Nov. 2001, pp. 749–752.
[61]
C. H. Taal, R. C. Hendriks, R. Heusdens, and J. Jensen, “An algorithm for intelligibility prediction of time-frequency weighted noisy speech,” IEEE Trans. Audio. Speech. Language Process., vol. 19, no. 7, pp. 2125–2136, Dec. 2011.
[62]
T. Drugman, Y. Stylianou, Y. Kida, and M. Akamine, “Voice activity detection: Merging source and filter-based information,” IEEE Signal Process. Lett., vol. 23, no. 2, pp. 252–256, Feb. 2016.
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