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Open AccessOpen Access JUSTC Information Science and Technology 02 May 2023

Low-complexity energy-aware sensor selection for noise reduction in distributed microphone networks

  • National Engineering Research Center for Speech and Language Information Processing, University of Science and Technology of China, Hefei 230027, China

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https://doi.org/10.52396/JUSTC-2022-0121
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  • Author Bio:

    Jie Zhang received the B.S. degree, master’s degree, and Ph.D. degree in Electrical Engineering from the Yunnan University, Peking University, and the Delft University of Technology in 2012, 2015, and 2020, respectively. He is currently an Associate Researcher in the National Engineering Research Center for Speech and Language Information Processing (NERC-SLIP), Faculty of Information Science and Technology, University of Science and Technology of China. He received the Best Student Paper Award for his publication at the 10th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). His team also won several awards in speech-related academic competitions (e.g., DiCOVA-ICASSP2022, NIST OpenASR2021, L3DAS23). His current research interests include multi-microphone speech processing, binaural auditory, speech recognition, and wireless (acoustic) sensor networks

  • Corresponding author: E-mail: jzhang6@ustc.edu.cn
  • Received Date: 14 September 2022
  • Accepted Date: 29 November 2022
    • Available Online: 02 May 2023
    Abstract

  • Abstract

    Noise reduction (NR) is a necessary front-end in many audio applications for improving signal quality. It was shown that sparsity-promoting sensor selection potentially makes a trade-off between energy consumption and NR performance, which is rather important for large-scale wireless acoustic sensor networks (WASNs), where many sensors contribute negligibly to NR but energy consumption affects the lifetime of WASNs. This paper presents a sensor selection approach for beamforming-based NR by minimizing the total energy consumption and constraining the output noise variance. Motivated by the optimal semi-definite programming (SDP) solution and the utility-based method, we propose three low-complexity selection metrics: weighted utility, gradient, and weighted input signal-to-noise ratio (SNR). It is shown that the proposed weighted utility and gradient-based methods are near-optimal in performance but much faster than the SDP-based method, and the weighted SNR method has the lowest time complexity with a tiny performance sacrifice. Numerical results using a simulated WASN validate the superiority of the proposed approaches over conventional methods.

    Graphical abstract

    Spatial sparse sensor selection for MVDR beamforming.

    Abstract

    Noise reduction (NR) is a necessary front-end in many audio applications for improving signal quality. It was shown that sparsity-promoting sensor selection potentially makes a trade-off between energy consumption and NR performance, which is rather important for large-scale wireless acoustic sensor networks (WASNs), where many sensors contribute negligibly to NR but energy consumption affects the lifetime of WASNs. This paper presents a sensor selection approach for beamforming-based NR by minimizing the total energy consumption and constraining the output noise variance. Motivated by the optimal semi-definite programming (SDP) solution and the utility-based method, we propose three low-complexity selection metrics: weighted utility, gradient, and weighted input signal-to-noise ratio (SNR). It is shown that the proposed weighted utility and gradient-based methods are near-optimal in performance but much faster than the SDP-based method, and the weighted SNR method has the lowest time complexity with a tiny performance sacrifice. Numerical results using a simulated WASN validate the superiority of the proposed approaches over conventional methods.

    • Public Summary

    • Sensor selection is an effective tool to optimize the geometry of microphone networks and reduce the transmission cost, where many sensors contributes marginally to the task performance at hand.
    • Based on the existing semi-definite programming utility-based methods, in this work we propose three energy-efficient utilities (i.e., weighted utility, gradient and weight input SNR), based on which three corresponding low-complexity sensor selection approaches are proposed.
    • Results show that sensors around sources and the fusion center are more informative in the sense of performance and the proposed narrowband methods converge more faster.

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    • References(38)
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    Adulyasas A, Sun Z, Wang N. Connected coverage optimization for sensor scheduling in wireless sensor networks. IEEE Sensors Journal, 2015, 15 (17): 3877–3892. doi: 10.1109/JSEN.2015.2395958
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    Wang Q, Guo S, Yiu K F C. Distributed acoustic beamforming with blockchain protection. IEEE Transactions on Industrial Informatics, 2020, 16 (11): 7126–7135. doi: 10.1109/TII.2020.2975899
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    Zou Q, Zou X, Zhang M, et al. A robust speech detection algorithm in a microphone array teleconferencing system. In: 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Salt Lake City, USA: IEEE, 2001: 3025–3028.
    [7]
    Gustafsson S, Martin R, Vary P. Combined acoustic echo control and noise reduction for hands-free telephony. Signal Processing, 1998, 64 (1): 21–32. doi: 10.1016/S0165-1684(97)00173-4
    [8]
    Moore D C, McCowan I A. Microphone array speech recognition: Experiments on overlapping speech in meetings. In: 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Hong Kong, China: IEEE, 2003: V–497.
    [9]
    Lee S C, Chen B W, Wang J F. Noisy environment-aware speech enhancement for speech recognition in human-robot interaction application. In: 2010 IEEE International Conference on Systems, Man and Cybernetics. Istanbul: IEEE, 2010: 3938–3941.
    [10]
    Amini J, Hendriks R C, Heusdens R, et al. Spatially correct rate-constrained noise reduction for binaural hearing aids in wireless acoustic sensor networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2020, 28: 2731–2742. doi: 10.1109/TASLP.2020.3028264
    [11]
    Zeng Y, Hendriks R C. Distributed delay and sum beamformer for speech enhancement via randomized gossip. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2014, 22 (1): 260–273. doi: 10.1109/TASLP.2013.2290861
    [12]
    Guan Q, Ji F, Liu Y, et al. Distance-vector-based opportunistic routing for underwater acoustic sensor networks. IEEE Internet of Things Journal, 2019, 6: 3831–3839. doi: 10.1109/JIOT.2019.2891910
    [13]
    Benesty J, Makino S, Chen J. Speech Enhancement. Berlin: Springer, 2005.
    [14]
    Zhang J, Heusdens R, Hendriks R C. Rate-distributed spatial filtering based noise reduction in wireless acoustic sensor networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018, 26 (11): 2015–2026. doi: 10.1109/TASLP.2018.2851157
    [15]
    Joshi S, Boyd S. Sensor selection via convex optimization. IEEE Transactions on Signal Processing, 2009, 57 (2): 451–462. doi: 10.1109/TSP.2008.2007095
    [16]
    Chepuri S P, Leus G. Sparsity-promoting sensor selection for non-linear measurement models. IEEE Transactions on Signal Processing, 2015, 63 (3): 684–698. doi: 10.1109/TSP.2014.2379662
    [17]
    Golovin D, Faulkner M, Krause A, Online distributed sensor selection. In: IPSN '10: Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks. New York: ACM, 2010: 220–231.
    [18]
    Zhang H, Moura J M F, Krogh B. Dynamic field estimation using wireless sensor networks: Tradeoffs between estimation error and communication cost. IEEE Transactions on Signal Processing, 2009, 57 (6): 2383–2395. doi: 10.1109/TSP.2009.2015110
    [19]
    Liu S, Chepuri S P, Fardad M, et al. Sensor selection for estimation with correlated measurement noise. IEEE Transactions on Signal Processing, 2016, 64: 3509–3522. doi: 10.1109/TSP.2016.2550005
    [20]
    Bertrand A, Moonen M. Efficient sensor subset selection and link failure response for linear MMSE signal estimation in wireless sensor networks. In: 2010 18th European Signal Processing Conference. Aalborg, Denmark : IEEE, 2010: 1092–1096.
    [21]
    Szurley J, Bertrand A, Moonen M, et al. Energy aware greedy subset selection for speech enhancement in wireless acoustic sensor networks. In: 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO). Bucharest, Romania: IEEE, 2012: 789–793.
    [22]
    Bertrand A. Utility metrics for assessment and subset selection of input variables for linear estimation [tips & tricks]. IEEE Signal Processing Magazine, 2018, 35 (6): 93–99. doi: 10.1109/MSP.2018.2856632
    [23]
    Zhang J, Chepuri S P, Hendriks R C, et al. Microphone subset selection for MVDR beamformer-based noise reduction. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018, 26 (3): 550–563. doi: 10.1109/TASLP.2017.2786544
    [24]
    Zhang J, Du J, Dai L R. Sensor selection for relative acoustic transfer function steered linearly-constrained beamformers. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 29: 1220–1232. doi: 10.1109/TASLP.2021.3064399
    [25]
    Zhang J, Zhang G, Dai L. Frequency-invariant sensor selection for MVDR beamforming in wireless acoustic sensor networks. IEEE Transactions on Wireless Communications, 2022, 21: 10648–10661. doi: 10.1109/TWC.2022.3185713
    [26]
    Bertrand A, Szurley J, Ruckebusch P, et al. Efficient calculation of sensor utility and sensor removal in wireless sensor networks for adaptive signal estimation and beamforming. IEEE Transactions on Signal Processing, 2012, 60 (11): 5857–5869. doi: 10.1109/TSP.2012.2210888
    [27]
    Zhang J, Chen H, Dai L R, et al. A study on reference microphone selection for multi-microphone speech enhancement. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 29: 671–683. doi: 10.1109/TASLP.2020.3039930
    [28]
    Zhang J, Heusdens R, Hendriks R C. Relative acoustic transfer function estimation in wireless acoustic sensor networks. IEEE/ACM Transactions on Audio, Speech and Language Processing, 2019, 27 (10): 1507–1519. doi: 10.1109/TASLP.2019.2923542
    [29]
    Frost O L. An algorithm for linearly constrained adaptive array processing. Proceedings of the IEEE, 1972, 60 (8): 926–935. doi: 10.1109/PROC.1972.8817
    [30]
    Van Veen B, Buckley K. Beamforming: A versatile approach to spatial filtering. IEEE ASSP Magazine, 1988, 5 (2): 4–24. doi: 10.1109/53.665
    [31]
    Capon J. High-resolution frequency-wavenumber spectrum analysis. Proceedings of the IEEE, 1969, 57 (8): 1408–1418. doi: 10.1109/PROC.1969.7278
    [32]
    Ciullo D, Celik G D, Modiano E. Minimizing transmission energy in sensor networks via trajectory control. In: 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks. Avignon, France: IEEE, 2010: 132–141.
    [33]
    Petersen K B, Pedersen M S. The Matrix Cookbook. Technical University of Denmark, 2008: 15.
    [34]
    Boyd S, Vandenberghe L. Convex optimization. Cambridge, UK: Cambridge University Press, 2004.
    [35]
    Hendriks R C, Heusdens R, Jensen J. MMSE based noise PSD tracking with low complexity. In: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. Dallas, USA: IEEE, 2010: 4266–4269.
    [36]
    Garofolo J, Lamel L, Fisher W, et al. DARPA TIMIT acoustic-phonetic speech database. National Institute of Standards and Technology (NIST), 1988, 15: 29–50.
    [37]
    Varga A, Steeneken H J M. Assessment for automatic speech recognition II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recognition systems. Speech Communication, 1993, 12 (3): 247–251. doi: 10.1016/0167-6393(93)90095-3
    [38]
    Allen J B, Berkley D A. Image method for efficiently simulating small-room acoustics. The Journal of the Acoustical Society of America, 1979, 65 (4): 943. doi: 10.1121/1.382599
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    Figure  1.  Sensor selection examples of the model- and data-driven approaches for $ \alpha=0.6 $. Note that active sensors are required by the data-driven methods, but are not required by the model-based counterparts.

    Figure  2.  The output noise and energy cost of data-driven approaches vs $ \alpha $.

    Figure  3.  The time consumption for performance requirement vs $ \alpha $.

    [1]
    Haller S, Karnouskos S, Schroth C. The Internet of things in an enterprise context. In: Domingue J, Fensel D, Traverso P, editors. Future Internet–FIS 2008. Berlin: Springer, 2008.
    [2]
    Adulyasas A, Sun Z, Wang N. Connected coverage optimization for sensor scheduling in wireless sensor networks. IEEE Sensors Journal, 2015, 15 (17): 3877–3892. doi: 10.1109/JSEN.2015.2395958
    [3]
    Turchet L, Fazekas G, Lagrange M, et al. The Internet of audio things: State of the art, vision, and challenges. IEEE Internet of Things Journal, 2020, 7 (10): 10233–10249. doi: 10.1109/JIOT.2020.2997047
    [4]
    Meng Y, Wang Z, Zhang W, et al. WiVo: Enhancing the security of voice control system via wireless signal in IoT environment. In: Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing. New York: ACM, 2018: 81–90.
    [5]
    Wang Q, Guo S, Yiu K F C. Distributed acoustic beamforming with blockchain protection. IEEE Transactions on Industrial Informatics, 2020, 16 (11): 7126–7135. doi: 10.1109/TII.2020.2975899
    [6]
    Zou Q, Zou X, Zhang M, et al. A robust speech detection algorithm in a microphone array teleconferencing system. In: 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Salt Lake City, USA: IEEE, 2001: 3025–3028.
    [7]
    Gustafsson S, Martin R, Vary P. Combined acoustic echo control and noise reduction for hands-free telephony. Signal Processing, 1998, 64 (1): 21–32. doi: 10.1016/S0165-1684(97)00173-4
    [8]
    Moore D C, McCowan I A. Microphone array speech recognition: Experiments on overlapping speech in meetings. In: 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Hong Kong, China: IEEE, 2003: V–497.
    [9]
    Lee S C, Chen B W, Wang J F. Noisy environment-aware speech enhancement for speech recognition in human-robot interaction application. In: 2010 IEEE International Conference on Systems, Man and Cybernetics. Istanbul: IEEE, 2010: 3938–3941.
    [10]
    Amini J, Hendriks R C, Heusdens R, et al. Spatially correct rate-constrained noise reduction for binaural hearing aids in wireless acoustic sensor networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2020, 28: 2731–2742. doi: 10.1109/TASLP.2020.3028264
    [11]
    Zeng Y, Hendriks R C. Distributed delay and sum beamformer for speech enhancement via randomized gossip. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2014, 22 (1): 260–273. doi: 10.1109/TASLP.2013.2290861
    [12]
    Guan Q, Ji F, Liu Y, et al. Distance-vector-based opportunistic routing for underwater acoustic sensor networks. IEEE Internet of Things Journal, 2019, 6: 3831–3839. doi: 10.1109/JIOT.2019.2891910
    [13]
    Benesty J, Makino S, Chen J. Speech Enhancement. Berlin: Springer, 2005.
    [14]
    Zhang J, Heusdens R, Hendriks R C. Rate-distributed spatial filtering based noise reduction in wireless acoustic sensor networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018, 26 (11): 2015–2026. doi: 10.1109/TASLP.2018.2851157
    [15]
    Joshi S, Boyd S. Sensor selection via convex optimization. IEEE Transactions on Signal Processing, 2009, 57 (2): 451–462. doi: 10.1109/TSP.2008.2007095
    [16]
    Chepuri S P, Leus G. Sparsity-promoting sensor selection for non-linear measurement models. IEEE Transactions on Signal Processing, 2015, 63 (3): 684–698. doi: 10.1109/TSP.2014.2379662
    [17]
    Golovin D, Faulkner M, Krause A, Online distributed sensor selection. In: IPSN '10: Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks. New York: ACM, 2010: 220–231.
    [18]
    Zhang H, Moura J M F, Krogh B. Dynamic field estimation using wireless sensor networks: Tradeoffs between estimation error and communication cost. IEEE Transactions on Signal Processing, 2009, 57 (6): 2383–2395. doi: 10.1109/TSP.2009.2015110
    [19]
    Liu S, Chepuri S P, Fardad M, et al. Sensor selection for estimation with correlated measurement noise. IEEE Transactions on Signal Processing, 2016, 64: 3509–3522. doi: 10.1109/TSP.2016.2550005
    [20]
    Bertrand A, Moonen M. Efficient sensor subset selection and link failure response for linear MMSE signal estimation in wireless sensor networks. In: 2010 18th European Signal Processing Conference. Aalborg, Denmark : IEEE, 2010: 1092–1096.
    [21]
    Szurley J, Bertrand A, Moonen M, et al. Energy aware greedy subset selection for speech enhancement in wireless acoustic sensor networks. In: 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO). Bucharest, Romania: IEEE, 2012: 789–793.
    [22]
    Bertrand A. Utility metrics for assessment and subset selection of input variables for linear estimation [tips & tricks]. IEEE Signal Processing Magazine, 2018, 35 (6): 93–99. doi: 10.1109/MSP.2018.2856632
    [23]
    Zhang J, Chepuri S P, Hendriks R C, et al. Microphone subset selection for MVDR beamformer-based noise reduction. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018, 26 (3): 550–563. doi: 10.1109/TASLP.2017.2786544
    [24]
    Zhang J, Du J, Dai L R. Sensor selection for relative acoustic transfer function steered linearly-constrained beamformers. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 29: 1220–1232. doi: 10.1109/TASLP.2021.3064399
    [25]
    Zhang J, Zhang G, Dai L. Frequency-invariant sensor selection for MVDR beamforming in wireless acoustic sensor networks. IEEE Transactions on Wireless Communications, 2022, 21: 10648–10661. doi: 10.1109/TWC.2022.3185713
    [26]
    Bertrand A, Szurley J, Ruckebusch P, et al. Efficient calculation of sensor utility and sensor removal in wireless sensor networks for adaptive signal estimation and beamforming. IEEE Transactions on Signal Processing, 2012, 60 (11): 5857–5869. doi: 10.1109/TSP.2012.2210888
    [27]
    Zhang J, Chen H, Dai L R, et al. A study on reference microphone selection for multi-microphone speech enhancement. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 29: 671–683. doi: 10.1109/TASLP.2020.3039930
    [28]
    Zhang J, Heusdens R, Hendriks R C. Relative acoustic transfer function estimation in wireless acoustic sensor networks. IEEE/ACM Transactions on Audio, Speech and Language Processing, 2019, 27 (10): 1507–1519. doi: 10.1109/TASLP.2019.2923542
    [29]
    Frost O L. An algorithm for linearly constrained adaptive array processing. Proceedings of the IEEE, 1972, 60 (8): 926–935. doi: 10.1109/PROC.1972.8817
    [30]
    Van Veen B, Buckley K. Beamforming: A versatile approach to spatial filtering. IEEE ASSP Magazine, 1988, 5 (2): 4–24. doi: 10.1109/53.665
    [31]
    Capon J. High-resolution frequency-wavenumber spectrum analysis. Proceedings of the IEEE, 1969, 57 (8): 1408–1418. doi: 10.1109/PROC.1969.7278
    [32]
    Ciullo D, Celik G D, Modiano E. Minimizing transmission energy in sensor networks via trajectory control. In: 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks. Avignon, France: IEEE, 2010: 132–141.
    [33]
    Petersen K B, Pedersen M S. The Matrix Cookbook. Technical University of Denmark, 2008: 15.
    [34]
    Boyd S, Vandenberghe L. Convex optimization. Cambridge, UK: Cambridge University Press, 2004.
    [35]
    Hendriks R C, Heusdens R, Jensen J. MMSE based noise PSD tracking with low complexity. In: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. Dallas, USA: IEEE, 2010: 4266–4269.
    [36]
    Garofolo J, Lamel L, Fisher W, et al. DARPA TIMIT acoustic-phonetic speech database. National Institute of Standards and Technology (NIST), 1988, 15: 29–50.
    [37]
    Varga A, Steeneken H J M. Assessment for automatic speech recognition II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recognition systems. Speech Communication, 1993, 12 (3): 247–251. doi: 10.1016/0167-6393(93)90095-3
    [38]
    Allen J B, Berkley D A. Image method for efficiently simulating small-room acoustics. The Journal of the Acoustical Society of America, 1979, 65 (4): 943. doi: 10.1121/1.382599
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