| Issue |
Acta Acust.
Volume 10, 2026
Topical Issue - Modern approaches to Active Control of Sound and Vibration
|
|
|---|---|---|
| Article Number | 31 | |
| Number of page(s) | 12 | |
| DOI | https://doi.org/10.1051/aacus/2026027 | |
| Published online | 05 May 2026 | |
- S.J. Elliott: Signal Processing for Active Control, 1st edn. Signal Processing and Its Applications. Academic Press, 2001. [Google Scholar]
- H.F. Olson, E.G. May: Electronic sound absorber. Journal of the Acoustical Society of America 25, 6 (1953) 1130–1136. [Google Scholar]
- C. Boucher, S.J. Elliott, P.A. Nelson: Effect of errors in the plant model on the performance of algorithms for adaptive feedforward control. IEE Proceedings F (Radar and Signal Processing) 138, 4 (1991) 313. [Google Scholar]
- T.J. Sutton, S.J. Elliott, A.M. McDonald, T.J. Saunders: Active control of road noise inside vehicles. Noise Control Engineering Journal 42, 4 (1994) 137–147. [Google Scholar]
- W. Jung, S.J. Elliott, J. Cheer: Local active control of road noise inside a vehicle. Mechanical Systems and Signal Processing 121 (2019) 144–157. [CrossRef] [Google Scholar]
- J. Buck, D. Sachau: Active headrests with selective delayless subband adaptive filters in an aircraft cabin. Mechanical Systems and Signal Processing 148 (2021) 107164. [Google Scholar]
- J.Y. Oh, H.W. Jung, M.H. Lee, K.H. Lee, Y.J. Kang: Enhancing active noise control of road noise using deep neural network to update secondary path estimate in real time. Mechanical Systems and Signal Processing 206 (2024) 110940. [Google Scholar]
- S.J. Elliott, P. Joseph, A. Bullmore, P.A. Nelson: Active cancellation at a point in a pure tone diffuse sound field. Journal of Sound and Vibration 120, 1 (1988) 183–189. [CrossRef] [Google Scholar]
- S.J. Elliott, J. Garcia-Bonito: Active cancellation of pressure and pressure gradient in a diffuse sound field. Journal of Sound and Vibration 186, 4 (1995) 696–704. [Google Scholar]
- P. Joseph, S.J. Elliott, P.A. Nelson: Near field zones of quiet. Journal of Sound and Vibration 172, 5 (1994) 605–627. [Google Scholar]
- J. Garcia-Bonito, S.J. Elliott, C.C. Boucher: Generation of zones of quiet using a virtual microphone arrangement. Journal of the Acoustical Society of America 101, 6 (1997) 3498–3516. [Google Scholar]
- S.J. Elliott, J. Cheer: Modeling local active sound control with remote sensors in spatially random pressure fields. Journal of the Acoustical Society of America 137, 4 (2015) 1936–1946. [Google Scholar]
- B. Rafaely, S.J. Elliott, J. Garcia-Bonito: Broadband performance of an active headrest. Journal of the Acoustical Society of America 106, 2 (1999) 787–793. [Google Scholar]
- B. Rafaely: Zones of quiet in a broadband diffuse sound field. Journal of the Acoustical Society of America 110, 1 (2001) 296–302. [Google Scholar]
- Jiang, H. Tsuji, H. Ohmori, A. Sano: Adaptation for active noise control. IEEE Control Systems Magazine 17, 6 (1997) 36–47. [Google Scholar]
- Wang, W. Ren: Convergence analysis of the multi-variable filtered-X LMS algorithm with application to active noise control. IEEE Transactions on Signal Processing 47, 4 (1999) 1166–1169. [Google Scholar]
- J. Cheer, S. Daley: An investigation of delayless subband adaptive filtering for multi-input multi-output active noise control applications. IEEE Transactions on Audio, Speech and Language Processing 25, 2 (2017) 359–373. [Google Scholar]
- L. Yin, Z. Zhang, M. Wu, Z. Wang, C. Ma, S. Zhou, J. Yang: Adaptive parallel filter method for active cancellation of road noise inside vehicles. Mechanical Systems and Signal Processing 193 (2023) 110274. [Google Scholar]
- D. Moreau, B. Cazzolato, A. Zander, C. Petersen: A review of virtual sensing algorithms for active noise control. Algorithms 1, 2 (2008) 69–99. [CrossRef] [Google Scholar]
- S.J. Elliott, W. Jung, J. Cheer: Causality and robustness in the remote sensing of acoustic pressure, with application to local active sound control, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Brighton, United Kingdom, 2019, pp. 8484–8488. [Google Scholar]
- W. Jung, S.J. Elliott, J. Cheer: Combining the remote microphone technique with head-tracking for local active sound control. Journal of the Acoustical Society of America 142, 1 (2017) 298–307. [Google Scholar]
- C.D. Petersen, B.S. Cazzolato, A.C. Zander, C.H. Hansen: Active noise control at a moving location using virtual sensing, in: Proceedings of the 13th International Congress of Sound and Vibration (ICSV13). ICSV. Vol. 13, Vienna, Austria, 2006. [Google Scholar]
- F. Veronesi, C.K. Lai, J. Cheer: Interpolation between plant responses in a head-tracked local active noise control headrest system. Mechanical Systems and Signal Processing 240 (2025) 113401. [Google Scholar]
- S. Koyama, J. Brunnström, H. Ito, N. Ueno, H. Saruwatari: Spatial active noise control based on kernel interpolation of sound field. IEEE Transactions on Audio, Speech and Language Processing 29 (2021) 3052–3063. [Google Scholar]
- C.K. Lai, J. Cheer: A comparison between spatial interpolation approaches in a head-tracked active headrest system, in: Proceedings of the 11th Convention of the European Acoustics Association Forum Acusticum/EuroNoise 2025. European Acoustics Association, Málaga, Spain, 2025, pp. 23–30. [Google Scholar]
- Q. Kong, Y. Cao, T. Iqbal, Y. Wang, W. Wang, M.D. Plumbley: PANNs: large-scale pretrained audio neural networks for audio pattern recognition. IEEE Transactions on Audio, Speech and Language Processing 28 (2020) 2880–2894. [Google Scholar]
- DCASE: DCASE2025 Challenge - DCASE, https://dcase.community/challenge2025/index. [Google Scholar]
- R. Xie, A. Tu, C. Shi, S. Elliott, H. Li, L. Zhang: Cognitive virtual sensing technique for feedforward active noise control, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, 2024, pp. 981–985. [Google Scholar]
- B. Wang, D. Shi, Z. Luo, X. Shen, J. Ji, W.-S. Gan: Transferable selective virtual sensing active noise control technique based on metric learning, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India, 2025, pp. 1–5. [Google Scholar]
- E. Fernandez-Grande, X. Karakonstantis, D. Caviedes-Nozal, P. Gerstoft: Generative models for sound field reconstruction. Journal of the Acoustical Society of America 153, 2 (2023) 1179–1190. [Google Scholar]
- V.S. Paul, N. Hahn, P.A. Nelson: Learning from data-driven sound field estimation using complex-valued neural networks, in: Proceedings of the 11th Convention of the European Acoustics Association Forum Acusticum/EuroNoise 2025. European Acoustics Association, Málaga, Spain, 2025, pp. 4343–4350. [Google Scholar]
- S. Koyama, J.G.C. Ribeiro, T. Nakamura, N. Ueno, M. Pezzoli: Physics-informed machine learning for sound field estimation: fundamentals, state of the art, and challenges. IEEE Signal Processing Magazine 41, 6 (2024) 60–71. [Google Scholar]
- Y.A. Zhang, F. Ma, T.D. Abhayapala, P.N. Samarasinghe, A. Bastine: An active noise control system based on soundfield interpolation using a physics-informed neural network, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, 2024, pp. 506–510. [Google Scholar]
- S.-C. Huang, C.-H. Ma, Y.-C. Hsu, M.R. Bai: Feedforward active noise global control using a linearly constrained beamforming approach. Journal of Sound and Vibration 537 (2022) 117190. [Google Scholar]
- X. Xiao, S. Watanabe, H. Erdogan, L. Lu, J. Hershey, M.L. Seltzer, G. Chen, Y. Zhang, M. Mandel, D. Yu: Deep beamforming networks for multi-channel speech recognition, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, 2016, pp. 5745–5749. [Google Scholar]
- R. Gu, J. Wu, S.-X. Zhang, L. Chen, Y. Xu, M. Yu, D. Su, Y. Zou, D. Yu: End-to-end multi-channel speech separation, 2019, https://arxiv.org/abs/1905.06286. [Google Scholar]
- D. Lee, S. Kim, J.-W. Choi: Inter-channel Conv-TasNet for multichannel speech enhancement, 2021, https://arxiv.org/abs/2111.04312. [Google Scholar]
- J. Kim: Remote microphone sound-field virtual sensing method using neural network for active noise control system, Master’s thesis, Purdue University, West Lafayette, IN, USA, 2024. [Google Scholar]
- Roure, A. Albarrazin: The remote microphone technique for active noise control, in: INTER-NOISE and NOISE-CON Congress and Conference Proceedings, Fort Lauderdale, FL, USA, 1999, pp. 1233–1244. [Google Scholar]
- F. Holzmüller, A. Sontacchi: Pilot study on virtual sensing for active noise control, in: Proceedings of DAS/DAGA 2025. Vol. 51. DAGA e.V., Copenhagen, Denmark, 2025, pp. 744–747. [Google Scholar]
- F. Holzmüller, A. Sontacchi: Deep observation filter for virtual sensing in local active noise control, in: Proceedings of the 11th Convention of the European Acoustics Association Forum Acusticum/EuroNoise 2025. European Acoustics Association, Málaga, Spain, 2025, pp. 105–112. [Google Scholar]
- F. Holzmüller, A. Sontacchi: Obs-TasNet: code, checkpoints, and results, Zenodo, 2026, https://zenodo.org/records/18872900. [Google Scholar]
- C. Antoñanzas, M. Ferrer, M. de Diego, A. Gonzalez: Remote microphone technique for active noise control over distributed networks. IEEE Transactions on Audio, Speech and Language Processing 31 (2023) 1522–1535. [Google Scholar]
- S. Kim, M.E. Altinsoy: A complementary effect in active control of powertrain and road noise in the vehicle interior. IEEE Access 10 (2022) 27 121-27 135. [Google Scholar]
- D. Treyer, S. Gaulocher, S. Germann, E. Curiger: Towards the implementation of the noise-cancelling office chair: algorithms and practical aspects, in: Proceedings of the 23rd International Congress on Sound and Vibration (ICSV23), Athens, Greece, 2016. [Google Scholar]
- Y. Luo, N. Mesgarani: Conv-TasNet: surpassing ideal time-Frequency magnitude masking for speech separation. IEEE Transactions on Audio, Speech and Language Processing 27, 8 (2019) 1256–1266. [Google Scholar]
- C. Lea, R. Vidal, A. Reiter, G.D. Hager: Temporal convolutional networks: a unified approach to action segmentation, in: G. Hua, H. Jégou, Eds. Proceedings of the European Conference on Computer Vision (ECCV). Vol. 14. Springer, Amsterdam, The Netherlands, 2016, pp. 47–54. [Google Scholar]
- F. Chollet: Xception: deep learning with depthwise separable convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Honolulu, HI, USA, 2017, pp. 1800–1807. [Google Scholar]
- D.S. Williamson, Y. Wang, D. Wang: Complex ratio masking for monaural speech separation. IEEE Transactions on Audio, Speech and Language Processing 24, 3 (2016) 483–492. [Google Scholar]
- T.N. Sainath, R.J. Weiss, K.W. Wilson, A. Narayanan, M. Bacchiani, A. Senior: Speaker location and microphone spacing invariant acoustic modeling from raw multichannel waveforms, in: IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU). IEEE, Scottsdale, AZ, USA, 2015, pp. 30–36. [Google Scholar]
- D. Haider, F. Perfler, V. Lostanlen, M. Ehler, P. Balazs: Hold me tight: stable encoder-decoder design for speech enhancement, in: Interspeech 2024. ISCA, Kos Island, Greece, 2024, pp. 5013–5017. [Google Scholar]
- Y. Luo, N. Mesgarani: TaSNet: time-domain audio separation network for real-time, single-channel speech separation, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AL, Canada, 2018, pp. 696–700. [Google Scholar]
- J.L. Ba, J.R. Kiros, G.E. Hinton: Layer normalization, 2016, https://arxiv.org/abs/1607.06450. [Google Scholar]
- Y. Wu, K. He: Group normalization, in: Proceedings of the European Conference on Computer Vision (ECCV). Vol. 15. Springer, Munich, Germany, 2018. [Google Scholar]
- K. He, X. Zhang, S. Ren, J. Sun: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification, in: IEEE International Conference on Computer Vision (ICCV). IEEE, Santiago, Chile, 2015, pp. 1026–1034. [Google Scholar]
- Grimm, J. Luberadzka, V. Hohmann: A toolbox for rendering virtual acoustic environments in the context of audiology. Acta Acustica United with Acustica 105, 3 (2019) 566–578. [CrossRef] [Google Scholar]
- P. Guiraud, S. Hafezi, P.A. Naylor, A.H. Moore, J. Donley, V. Tourbabin, T. Lunner: An introduction to the speech enhancement for augmented reality (spear) challenge, in: IEEE International Workshop on Acoustic Signal Enhancement (IWAENC). Vol. 17. IEEE, Bamberg, Germany, 2022. [Google Scholar]
- S. Braun, I. Tashev: Data augmentation and loss normalization for deep noise suppression, in: A. Karpov, R. Potapova, Eds. Speech and Computer. Springer International Publishing, St. Petersburg, Russia, 2020, pp. 79–86. [Google Scholar]
- A.V. Oppenheim, R.W. Schafer: Discrete-Time Signal Processing, 3rd edn. Pearson, Upper Saddle River, NJ, USA, 2010. [Google Scholar]
- D.P. Kingma, J. Ba: Adam: a method for stochastic optimization, in: International Conference on Learning Representation (ICLR). Vol. 3, San Diega, CA, USA, 2015. [Google Scholar]
- P.D. Welch: The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on Audio and Electroacoustics 15, 2 (1967) 70–73. [CrossRef] [Google Scholar]
- Zhang, S.J. Elliott, J. Cheer: Robust performance of virtual sensing methods for active noise control. Mechanical Systems and Signal Processing 152 (2021) 107453. [Google Scholar]
- S. Elliott, I. Stothers, P. Nelson: A multiple error LMS algorithm and its application to the active control of sound and vibration. IEEE Transactions on Acoustics, Speech, and Signal Processing 35, 10 (1987) 1423–1434. [CrossRef] [Google Scholar]
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