Pith. sign in

REVIEW 3 major objections 4 minor 78 references

Meta-Learning-Based Delayless Subband Adaptive Filter using Complex Self-Attention for Active Noise Control

T0 review · 3 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A neural network trained as a meta-learning update rule outperforms classical adaptive filters in active noise control.

desk verdict A promising learned-update-rule ANC paper whose headline claim outpaces its evidence because the baselines are only two linear FxLMS variants. read the letter →

arxiv 2412.19471 v1 pith:OTUPXXPL submitted 2024-12-27 eess.AS cs.LGcs.SD

Pengxing Feng , Hing Cheung So This is my paper · ORCID
classification eess.AScs.LGcs.SD
keywords activenoisecontroladaptivefiltermeta-learningdelaylesssubbandcomplexself-attentionrecurrentneuralnetworknonlinearskipupdating
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that active noise control can be improved by replacing the hand-designed linear update rule of an adaptive filter with a neural network that learns the update rule itself. This neural network is trained using only noisy error observations, not clean target signals, and it adapts the filter weights to suppress noise in nonlinear and nonstationary environments. The authors report that their method, called MDSAF, reduces the normalized mean squared error by about 1 to 5 dB compared with the normalized filtered-x LMS and its delayless subband variant across four noise types, three signal-to-noise ratios, and several loudspeaker nonlinearity levels. If this holds, a data-driven update rule could make active noise control systems effective in conditions where classical linear updates struggle, such as loudspeaker saturation and sudden path changes.

What carries the argument

The central object is a neural network used as the adaptive filter's update rule. The network is a single-headed attention recurrent network (SHA-RNN) whose attention block uses learnable feature embeddings to weight the elements of the input vector, and its output is a complex-valued gradient that is amplitude-limited to act like a normalized step. This update rule is embedded in a modified delayless subband architecture: the filtered reference and error signals are split into subbands by an analysis filter bank, each subband weight is updated in the frequency domain, and the fullband filter is reconstructed by weight stacking and IFFT. The architecture also includes a skip-updating factor that reduces how often the network runs, relaxing the real-time constraint.

What would settle it

Run the trained MDSAF model on a real single-channel ANC setup with a physical loudspeaker, reference microphone, and error microphone, and compare its noise reduction against NFxLMS with a properly tuned step size; if the learned rule does not beat or match the baseline on real recorded noise, the simulation-to-real transfer claim fails. Alternatively, measure whether the model remains stable when the secondary path is replaced by a measured impulse response with a different delay than in training.

Watch

Extended reading notes

Core claim

In the paper's own terms, the authors establish that a meta-learning-based delayless subband adaptive filter, using a single-headed attention recurrent network with learnable feature embedding as the update rule, can adapt an ANC filter from noisy observations alone. The network predicts a frequency-domain gradient that updates the adaptive filter weights, and the delayless subband architecture plus a skip-updating strategy let the update happen less frequently without adding signal delay. Simulations with the fNSE loudspeaker saturation model show the learned rule achieves lower NMSE than NFxLMS and DSNFxLMS in all tested conditions, including when the primary path changes suddenly mid-test. A variant trained with only the main delay of the secondary path still outperforms the classical baselines, indicating the rule does not need exact secondary-path knowledge.

Load-bearing premise

The learned update rule transfers from the specific simulated room, noise set, and loudspeaker saturation model used in training to real acoustic environments and hardware.

Editorial extensions

If this is right

  • ANC controllers can be trained end-to-end from noisy data without clean reference signals, removing a major obstacle to deep learning in practical noise control.
  • The delayless subband and skip-updating design mean the learned update can run in real time on moderate hardware, since the update frequency drops by the downsampling factor.
  • The model's ability to adapt to sudden primary-path changes suggests learned update rules generalize to nonstationary acoustic environments better than fixed-linear algorithms.
  • Training with only the main delay of the secondary path indicates the approach can work when exact secondary-path identification is unavailable, easing deployment.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural next step is to test the trained update rule on real measured impulse responses and a physical loudspeaker; the biggest risk is that the simulation's room geometry and saturation model do not cover real acoustic variability.
  • The same meta-learning formulation could be applied to other adaptive filtering tasks, such as echo cancellation or feedback control, wherever a linear update rule is the bottleneck.
  • The reported 1–5 dB gain is over fixed-step-size classical baselines; an even more direct comparison would tune the baselines' step sizes per condition.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes a meta-learning-based delayless subband adaptive filter (MDSAF) for single-channel feedforward active noise control. The adaptive filter weights are updated by a recurrent neural network with a complex self-attention module, and the update rule is trained end-to-end from noisy error observations without oracle labels. A modified delayless subband architecture and a skip-updating strategy reduce the required update rate, and a variant (MDSAF-MD) is trained using only the main delay of the secondary path. The experiments compare MDSAF and MDSAF-MD with NFxLMS and DSNFxLMS on four NOISEX-92 noise types at SNRs of 5, 15, and 25 dB and nonlinearity levels eta^2 = 0.5, 2, and infinity, with a mid-test primary path change. The proposed methods consistently achieve lower NMSE than the two linear baselines by roughly 1-5 dB.

Significance. If the reported results hold, the idea of learning an adaptive-filter update rule for ANC from noisy observations is a valuable contribution, particularly for nonlinear loudspeaker saturation and nonstationary noise. The evaluation has genuine strengths: the update rule is trained on separate data (ESC-50 and Nonspeech) and tested on held-out NOISEX-92 noises and unseen primary-path changes; the method does not require true labels; and the paper includes a real-time complexity analysis with measured inference time. The main weakness is that the empirical claim of superiority over 'traditional methods' is broader than the baseline set actually tested. With additional baselines and a correction to the gradient post-processing formula, the central idea would be well supported.

major comments (3)
  1. [Section 4.3, Tables 2 and 3] The claimed superiority over 'traditional methods' is not supported by the baseline set. The only adaptive-filter competitors are NFxLMS and DSNFxLMS, both linear filters, and their step sizes are fixed at 0.01 for all noise types, SNRs, and nonlinearity levels. Since the paper's motivation is the failure of linear updating rules under loudspeaker saturation, and Section 1 surveys nonlinear traditional alternatives (Volterra FxLMS [18-20], tanh-based FxLMS [21-22], functional-link ANN [24-26]), the experiments need at least one nonlinear classical baseline with a tuned or per-condition step size. As it stands, Tables 2 and 3 demonstrate superiority over linear FxLMS with a fixed step size, not over the broader class of traditional nonlinear adaptive filters invoked in the abstract.
  2. [Section 3.3, Eq. (20)] Equation (20) does not implement the stated amplitude clamp and appears to be independent of g(n). For every |g| < exp(10), the expression max[min(|g|, exp(-10)), exp(10)] evaluates to exp(10), and for |g| >= exp(10) it also evaluates to exp(10); hence |~g(n)| is the constant ln(exp(10)/10)+1, approximately 8.7, not a value in [0,2] as claimed in the surrounding text. This makes the exact gradient post-processing used in training and testing ambiguous. Please correct the formula or the description and verify that the reported experiments use the corrected version.
  3. [Sections 3.1-3.3 and 4.3] The closest learned-update-rule baselines, the meta-learning adaptive filters of [48] and [49] from which the present architecture is directly derived, are not evaluated. Because the contribution is positioned as a meta-learning-based adaptive filter, a comparison with Meta-AF or an equivalent learned optimizer is necessary to show that the proposed self-attention and delayless subband modifications improve over the prior learned update rule, rather than only over classical linear FxLMS. Without this comparison, the novelty claim relative to [48,49] remains unquantified.
minor comments (4)
  1. [Tables 2 and 3] The tables report averaged NMSE over 50 independent runs but do not report standard deviations or confidence intervals; for differences that are sometimes around 1 dB, please add variability measures or a significance test.
  2. [Section 4.2] The abstract and contribution list claim robustness to 'various environments,' but all simulations use the same 5 m x 4 m x 3 m room and the same fNSE loudspeaker model; please temper the claim or add a second room geometry or a real-world recording.
  3. [Section 3.3] The text states that the compression in (17) 'does not improve performance' but is still applied; either remove it or provide an ablation, since as written this is contradictory.
  4. [Section 1, contribution list] The contribution bullet says the source code is available, but no URL appears in the manuscript; please include the link for reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the proposed NN update rule is trained on separate simulated data and its NMSE is measured on held-out noises, paths, and nonlinearity levels, so the central result is an empirical outcome rather than a restatement of the training objective.

full rationale

The paper's derivation chain is self-contained with respect to its empirical claim. The meta-learner in (8)-(9) predicts a gradient-like update from noisy observations, and the network parameters are optimized on the meta-loss (24), which is an accumulated squared-error over training batches drawn from ESC-50 and Nonspeech recordings in a simulated room. Evaluation in Tables 2 and 3 uses NOISEX-92 noise types, different primary-path geometries, held-out nonlinearity factors η2 = 0.5, 2, and ∞, and SNR values 5/15/25 dB, so the reported NMSE is a measured generalization outcome, not a fitted quantity. The fact that the training loss (23) and the evaluation metric (29) are both squared-error-based reflects standard objective alignment, not circularity: no test NMSE value is used to set any model parameter, and no equation is assumed and then re-derived. The meta-learning framework is adopted from the external prior work [49] by Casebeer et al., not from the present authors, so this is a normal literature dependency rather than a load-bearing self-citation. Likewise, the delayless subband structure ([58]) and SHA-RNN ([61]) are independent external building blocks. The remaining concerns—only two linear baselines with a fixed step size of 0.01 are compared, and all testing occurs in the same simulated room with the same fNSE model—affect the strength and generality of the empirical comparison, but they are correctness/fairness risks, not circularity, under the quoted-evidence standard.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The trained NN has many learned parameters, but they are fit on the training set, not to the test result. The hand-chosen hyperparameters listed above are the main free parameters. The acoustic and nonlinearity models are domain assumptions, and the training data representativeness is an ad hoc assumption for this paper. No new physical entities are introduced.

free parameters (2)
  • Step size mu for the update rule in Eq. (14) = 0.4
    The step size scaling the NN output in the update rule is chosen by hand and is not learned; it affects the scale of the update.
  • Output amplitude clamp bounds in Eq. (20) = 0 to 2
    The clamp limits the predicted gradient amplitude to the claimed ideal NFxLMS step size interval; chosen by hand.
assumptions (4)
  • domain assumption Image method RIR generation accurately represents the acoustic environment.
    All training and testing use the image method to simulate room impulse responses (Section 4.2); if this model diverges from real acoustics, the results may not transfer.
  • domain assumption Loudspeaker saturation is modeled by fNSE in Eq. (3).
    The nonlinearity in the secondary path is assumed to follow this integral model; real loudspeakers may have different saturation characteristics.
  • domain assumption The differentiable simulator permits backpropagation through acoustic paths and nonlinearity.
    Training relies on gradients through the entire signal chain (Table 1), requiring all components to be differentiable.
  • ad hoc to paper The training noise set (ESC-50 and Nonspeech) is representative enough for generalization to NOISEX-92.
    The paper selects these training corpora and assumes they cover the test noise types; no analysis shows distribution overlap.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Meta-Learning-Based Delayless Subband Adaptive Filter using Complex Self-Attention for Active Noise Control." pith.science (2026). https://pith.science/paper/OTUPXXPL

@misc{pith2026241219471,
  author       = {Pith},
  title        = {Pith review of: Meta-Learning-Based Delayless Subband Adaptive Filter using Complex Self-Attention for Active Noise Control},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OTUPXXPL}},
  note         = {Machine review of arXiv:2412.19471}
}
read the original abstract

Active noise control typically employs adaptive filtering to generate secondary noise, where the least mean square algorithm is the most widely used. However, traditional updating rules are linear and exhibit limited effectiveness in addressing nonlinear environments and nonstationary noise. To tackle this challenge, we reformulate the active noise control problem as a meta-learning problem and propose a meta-learning-based delayless subband adaptive filter with deep neural networks. The core idea is to utilize a neural network as an adaptive algorithm that can adapt to different environments and types of noise. The neural network will train under noisy observations, implying that it recognizes the optimized updating rule without true labels. A single-headed attention recurrent neural network is devised with learnable feature embedding to update the adaptive filter weight efficiently, enabling accurate computation of the secondary source to attenuate the unwanted primary noise. In order to relax the time constraint on updating the adaptive filter weights, the delayless subband architecture is employed, which will allow the system to be updated less frequently as the downsampling factor increases. In addition, the delayless subband architecture does not introduce additional time delays in active noise control systems. A skip updating strategy is introduced to decrease the updating frequency further so that machines with limited resources have more possibility to board our meta-learning-based model. Extensive multi-condition training ensures generalization and robustness against various types of noise and environments. Simulation results demonstrate that our meta-learning-based model achieves superior noise reduction performance compared to traditional methods.

Figures

Figures reproduced from arXiv: 2412.19471 by the authors.

Figure 1
Figure 1. Diagram of single-channel feedforward ANC system [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Diagram of ANC system with adaptive algorithm [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Diagram of ANC system with NN model using meta learning [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Diagram of modified delayless subband architecture with NN model [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Proposed neural network architecture with complex self-attention, (x, y) [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Proposed SHA-RNN module with learnable feature embedding , (x, y) means [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: NMSEs under different noise types at SNR= 5 dB and [PITH_FULL_IMAGE:figures/full_fig_p021_7.png]
Figure 8
Figure 8. Figure 8: PSDs under different noise types at SNR= 5 dB and [PITH_FULL_IMAGE:figures/full_fig_p022_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

78 extracted references · 76 canonical work pages

  1. [48]

    Casebeer, N

    J. Casebeer, N. J. Bryan, P. Smaragdis, Auto-DSP: Learning to optimize acoustic echo cancellers, in: 2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, (2021), pp. 291–295

    work page 2021

  2. [49]

    Casebeer, N

    J. Casebeer, N. J. Bryan, P. Smaragdis, Meta-AF: Meta-learning for adaptive filters, IEEE/ACM Trans. Audio Speech Lang. Process 31 (2022) 355–370

    work page 2022

  3. [1]

    S. M. Kuo, D. R. Morgan, Active Noise Control Systems, Wiley, New York, (1996)

    work page 1996

  4. [2]

    Elliott, Signal Processing for Active Control, Academic Press, San Diego, (2000)

    S. Elliott, Signal Processing for Active Control, Academic Press, San Diego, (2000)

    work page 2000

  5. [3]

    G. C. Goodwin, E. I. Silva, D. E. Quevedo, Analysis and design of networked control systems using the additive noise model methodology, Asian J. Control 12 (4) (2010) 443–459

    work page 2010

  6. [4]

    J. Lee, G. W. Swenson, Compact sound absorbers for low frequencies, Noise Control Eng. J. 38 (3) (1992) 109–117

    work page 1992

  7. [5]

    Y. Tao, M. Ren, H. Zhang, T. Peijs, Recent progress in acoustic materi- als and noise control strategies–a review, Appl. Mater. Today 24 (2021) 101141

    work page 2021

  8. [6]

    D. Shi, B. Lam, W.-S. Gan, J. Cheer, S. J. Elliott, Active noise control in the new century: The role and prospect of signal processing, in: INTER- NOISE and NOISE-CON Congress and Conference Proceedings, Vol. 268, Institute of Noise Control Engineering, 2023, pp. 5141–5151

    work page 2023

Show all 78 references
  1. [7]

    Zhang, M

    Z. Zhang, M. Wu, L. Yin, C. Gong, J. Wang, S. Zhou, J. Yang, Robust feedback controller combined with the remote microphone method for broadband active noise control in headrest, Appl. Acoust. 195 (2022) 108815

    2022

  2. [8]

    Cheng, Z

    C. Cheng, Z. Liu, X. Li, C. Lu, W. Chen, An optimal sensor layout method based on noise reduction estimation for active road noise control, Mech. Syst. Signal Process. 220 (2024) 111668

    2024

  3. [9]

    S. M. Kuo, D. R. Morgan, Active noise control: A tutorial review, Pro- ceedings of the IEEE 87 (6) (1999) 943–973

    1999

  4. [10]

    Y. Song, Y. Gong, S. M. Kuo, A robust hybrid feedback active noise cancellation headset, IEEE Trans. Speech Audio Process. 13 (4) (2005) 607–617

    2005

  5. [11]

    Ingle, S

    V. Ingle, S. Kogon, D. Manolakis, Statisical and Adaptive Signal Pro- cessing, Artech, (2005). 24

    2005

  6. [12]

    Haykin, Adaptive Filter Theory, Pearson, San Francisco, 2002

    S. Haykin, Adaptive Filter Theory, Pearson, San Francisco, 2002

    2002

  7. [13]

    S. Gaur, V. Gupta, A review on filtered-x LMS algorithm, Int. J. Signal Process. Syst. 4 (2) (2016) 172–176

    2016

  8. [14]

    G. Chen, T. Sone, N. Saito, M. Abe, S. Makino, The stability and con- vergence characteristics of the delayed-x LMS algorithm in ANC sys- tems, J. Sound Vib. 216 (4) (1998) 637–648

    1998

  9. [15]

    E. A. Manzano, J. Tafur, Optimal step size for a delayed FxLMS algorithm applied in a prototype of active noise control system, in: 2018 IEEE 14th International Conference on Control and Automation, (2018), pp. 935–940

    2018

  10. [16]

    O. J. Tobias, R. Seara, Performance comparison of the FXLMS, non- linear FXLMS and leaky FXLMS algorithms in nonlinear active con- trol applications, in: 2002 11th European Signal Processing Conference, (2002), pp. 1–4

    2002

  11. [17]

    O. J. Tobias, R. Seara, Leaky-FXLMS algorithm: Stochastic analysis for Gaussian data and secondary path modeling error, IEEE Trans. on Speech and Audio process. 13 (6) (2005) 1217–1230

    2005

  12. [18]

    L.-Z. Tan, J. Jiang, Filtered-x second-order Volterra adaptive algo- rithms, Electronics Lett. 33 (8) (1997) 671–672

    1997

  13. [19]

    L. Tan, J. Jiang, Adaptive Volterra filters for active control of nonlinear noise processes, IEEE Trans. Signal Process. 49 (8) (2001) 1667–1676

    2001

  14. [20]

    H. Zhao, X. Zeng, X. Zhang, Z. He, T. Li, W. Zhao, Adaptive ex- tended pipelined second-order Volterra filter for nonlinear active noise controller, IEEE Trans. Audio Speech Language Process. 20 (4) (2011) 1394–1399

    2011

  15. [21]

    M. A. Sahib, R. Kamil, M. H. Marhaban, Nonlinear FXLMS algo- rithm for active noise control systems with saturation nonlinearity, IEEJ Trans. Electr. Electr. 7 (6) (2012) 598–606

    2012

  16. [22]

    Ghasemi, R

    S. Ghasemi, R. Kamil, M. H. Marhaban, Nonlinear Thf-FxLMS algo- rithm for active noise control with loudspeaker nonlinearity, Asian J. Control 18 (2) (2016) 502–513. 25

    2016

  17. [23]

    Zhou, Q.-Z

    Y.-L. Zhou, Q.-Z. Zhang, X.-D. Li, W.-S. Gan, Analysis and DSP im- plementation of an ANC system using a filtered-error neural network, J. Sound Vib. 285 (1-2) (2005) 1–25

    2005

  18. [24]

    S. K. Behera, D. P. Das, B. Subudhi, Functional link artificial neural network applied to active noise control of a mixture of tonal and chaotic noise, Appl. Soft Comput. 23 (2014) 51–60

    2014

  19. [25]

    D. C. Le, J. Zhang, Y. Pang, A bilinear functional link artificial neural network filter for nonlinear active noise control and its stability condi- tion, Appl. Acoust. 132 (2018) 19–25

    2018

  20. [26]

    Y. Zhu, H. Zhao, S. S. Bhattacharjee, M. G. Christensen, Quantized information-theoretic learning based Laguerre functional linked neural networks for nonlinear active noise control, Mech. Syst. Signal Process. 213 (2024) 111348

    2024

  21. [27]

    S. D. Snyder, N. Tanaka, Active control of vibration using a neural network, IEEE Trans. Neural Netw. 6 (4) (1995) 819–828

    1995

  22. [28]

    Bouchard, B

    M. Bouchard, B. Paillard, C. T. Le Dinh, Improved training of neural networks for the nonlinear active control of sound and vibration, IEEE Trans. Neural Netw. 10 (2) (1999) 391–401

    1999

  23. [29]

    Zhang, W.-S

    Q.-Z. Zhang, W.-S. Gan, Y.-l. Zhou, Adaptive recurrent fuzzy neural networks for active noise control, J. Sound Vib. 296 (4-5) (2006) 935– 948

    2006

  24. [30]

    Kumar, S

    K. Kumar, S. S. Bhattacharjee, N. V. George, Modified Champernowne function based robust and sparsity-aware adaptive filters, IEEE Trans. Circuits Syst. II Express Briefs 68 (6) (2020) 2202–2206

    2020

  25. [31]

    J. Yang, Q. Zhang, Y. Luo, S. Yan, A fractional-order gradient-descent total least mean p-norm adaptive filtering algorithm in impulsive noise environments, IEEE Trans. Circuits Syst. II Express Briefs 70 (3) (2022) 1204–1208

    2022

  26. [32]

    Patel, S

    V. Patel, S. S. Bhattacharjee, M. G. Christensen, Generalized soft-root- sign based robust sparsity-aware adaptive filters, IEEE Signal Process. Lett. 30 (2023) 200–204. 26

    2023

  27. [33]

    P. Feng, L. Zhang, D. Meng, X. Pi, An active noise control algorithm based on fractional lower order covariance with on-line characteristics estimation, Mech. Syst. Sig. Process. 186 (2023) 109835

    2023

  28. [34]

    V. Zue, S. Seneff, J. Glass, Speech database development at MIT: TIMIT and beyond, Speech Commun. 9 (4) (1990) 351–356

    1990

  29. [35]

    Kapitanov, K

    A. Kapitanov, K. Kvanchiani, A. Nagaev, R. Kraynov, A. Makhliarchuk, HaGRID–Hand gesture recognition image dataset, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, (2024), pp. 4572–4581

    2024

  30. [36]

    L. Deng, D. Yu, et al., Deep learning: methods and applications, Foun- dations and trends ® in signal processing 7 (3–4) (2014) 197–387

    2014

  31. [37]

    Z. Li, F. Liu, W. Yang, S. Peng, J. Zhou, A survey of convolutional neural networks: Analysis, applications, and prospects, IEEE Trans. Neural Netw. Learn. Syst. 33 (12) (2021) 6999–7019

    2021

  32. [38]

    Graves, A

    A. Graves, A. Graves, Long short-term memory, Supervised Sequence Labelling with Recurrent Neural Networks (2012) 37–45

    2012

  33. [39]

    R. Fu, Z. Zhang, L. Li, Using LSTM and GRU neural network meth- ods for traffic flow prediction, in: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation, IEEE, (2016), pp. 324–328

    2016

  34. [40]

    Y. Duan, L. Yisheng, F.-Y. Wang, Travel time prediction with LSTM neural network, in: 2016 IEEE 19th International Conference on Intel- ligent Transportation Systems, (2016), pp. 1053–1058

    2016

  35. [41]

    S. Park, E. Patterson, C. Baum, Long short-term memory and convolu- tional neural networks for active noise control, in: 2019 5th International Conference on Frontiers of Signal Processing, (2019), pp. 121–125

    2019

  36. [42]

    Zhang, D

    H. Zhang, D. Wang, Deep ANC: A deep learning approach to active noise control, Neural Netw. 141 (2021) 1–10

    2021

  37. [43]

    Zhang, D

    H. Zhang, D. Wang, Deep MCANC: A deep learning approach to multi- channel active noise control, Neural Netw. 158 (2023) 318–327. 27

    2023

  38. [44]

    Z. Luo, D. Shi, X. Shen, J. Ji, W.-S. Gan, Deep generative fixed-filter ac- tive noise control, in: 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, 2023, pp. 1–5

    2023

  39. [45]

    Z. Luo, D. Shi, W.-S. Gan, Q. Huang, Delayless generative fixed-filter ac- tive noise control based on deep learning and bayesian filter, IEEE/ACM Trans. Audio. Speech. Lang. Process. (2023)

    2023

  40. [46]

    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, Mech. Syst. Signal Process. 206 (2024) 110940

    2024

  41. [47]

    Zhang, S

    H. Zhang, S. Kandadai, H. Rao, M. Kim, T. Pruthi, T. Kristjansson, Deep adaptive AEC: Hybrid of deep learning and adaptive acoustic echo cancellation, in: 2022 IEEE International Conference on Acous- tics, Speech and Signal Processing, (2022), pp. 756–760

    2022

  42. [50]

    F. Yang, Y. Cao, M. Wu, F. Albu, J. Yang, Frequency-domain filtered-x LMS algorithms for active noise control: A review and new insights, Appl. Sci. 8 (11) (2018) 2313

    2018

  43. [51]

    F. Yang, J. Guo, J. Yang, Stochastic analysis of the filtered-x LMS algorithm for active noise control, IEEE/ACM Trans. Audio Speech. Lang. Process. 28 (2020) 2252–2266

    2020

  44. [52]

    O. J. Tobias, R. Seara, On the LMS algorithm with constant and variable leakage factor in a nonlinear environment, IEEE Trans. Signal Process. 54 (9) (2006) 3448–3458

    2006

  45. [53]

    G. Sun, T. Feng, M. Li, T. C. Lim, Convergence analysis of FxLMS- based active noise control for repetitive impulses, Appl. Acoust. 89 (2015) 178–187. 28

    2015

  46. [54]

    I. T. Ardekani, W. H. Abdulla, Theoretical convergence analysis of FxLMS algorithm, Signal Process. 90 (12) (2010) 3046–3055

    2010

  47. [55]

    Hospedales, A

    T. Hospedales, A. Antoniou, P. Micaelli, A. Storkey, Meta-learning in neural networks: A survey, IEEE Trans. Pattern Anal. Mach. Intell. 44 (9) (2021) 5149–5169

    2021

  48. [56]

    C. Finn, A. Rajeswaran, S. Kakade, S. Levine, Online meta-learning, in: Proc. Int. Conf. Mach. Learn., PMLR, (2019), pp. 1920–1930

    2019

  49. [57]

    Tokhi, R

    M. Tokhi, R. Wood, Active noise control using multi-layered perceptron neural networks, J. Low. Freq. Noise V. A 16 (2) (1997) 109–144

    1997

  50. [58]

    D. R. Morgan, J. C. Thi, A delayless subband adaptive filter architec- ture, IEEE Trans Signal Process. 43 (8) (1995) 1819–1830

    1995

  51. [59]

    X. Li, C. Lu, W. Chen, Z. Liu, C. Cheng, Y. Wang, S. Du, Enhanced se- lective delayless subband algorithm independent of primary disturbance configuration for multi-channel active noise control system in vehicles, Mech. Syst. Signal Process. 216 (2024) 111456

    2024

  52. [60]

    Andrychowicz, M

    M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau, T. Schaul, B. Shillingford, N. De Freitas, Learning to learn by gradient descent by gradient descent, Adv. Neural Inf. Process. Syst. 29 (2016)

    2016

  53. [61]

    Merity, Single headed attention RNN: Stop thinking with your head, arXiv preprint arXiv:1911.11423 (2019)

    S. Merity, Single headed attention RNN: Stop thinking with your head, arXiv preprint arXiv:1911.11423 (2019)

    2019 arXiv

  54. [62]

    Vaswani, N

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, Attention is all you need, Adv. Neural Inf. Process. Syst. 30 (2017)

    2017

  55. [63]

    K. He, X. Zhang, S. Ren, J. Sun, Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification, in: Proc. IEEE Int. Confe. Computer Vision, (2015), pp. 1026–1034

    2015

  56. [64]

    M. Wu, G. Chen, X. Qiu, An improved active noise control algorithm without secondary path identification based on the frequency-domain subband architecture, IEEE Trans. Audio Speech Language Process. 16 (8) (2008) 1409–1419. 29

    2008

  57. [65]

    M. Gao, J. Lu, X. Qiu, A simplified subband ANC algorithm without secondary path modeling, IEEE/ACM Trans. Audio Speech Language Process. 24 (7) (2016) 1164–1174

    2016

  58. [66]

    Duchi, E

    J. Duchi, E. Hazan, Y. Singer, Adaptive subgradient methods for online learning and stochastic optimization, J. Mach. Learn. Res. 12 (7) (2011)

    2011

  59. [67]

    Tieleman, Lecture 6.5-rmsprop: Divide the gradient by a running av- erage of its recent magnitude, COURSERA: Neural Netw

    T. Tieleman, Lecture 6.5-rmsprop: Divide the gradient by a running av- erage of its recent magnitude, COURSERA: Neural Netw. Mach. Learn. 4 (2) (2012) 26

    2012

  60. [68]

    D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980 (2014)

    2014 arXiv

  61. [69]

    O. R. developers, ONNX runtime, https://onnxruntime.ai/, version: x.y.z (2021)

    2021

  62. [70]

    K. J. Piczak, ESC: Dataset for environmental sound classification, in: Proc. 23rd ACM Int. Conf. Multimedia, 2015, pp. 1015–1018

    2015

  63. [71]

    G. Hu, D. Wang, Segregation of unvoiced speech from nonspeech inter- ference, J. Acoust. Soc. Am. 124 (2) (2008) 1306–1319

    2008

  64. [72]

    Varga, H

    A. Varga, H. J. Steeneken, 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 Commun. 12 (3) (1993) 247–251

    1993

  65. [73]

    C. D. Kestell, Active control of sound in a small single engine aircraft cabin with virtual error sensors, Ph.D. thesis, University of Adelaide (2000)

    2000

  66. [74]

    Cheer, Active control of the acoustic environment in an automobile cabin, Ph.D

    J. Cheer, Active control of the acoustic environment in an automobile cabin, Ph.D. thesis, University of Southampton (2012)

    2012

  67. [75]

    J. B. Allen, D. A. Berkley, Image method for efficiently simulating small- room acoustics, J. Acoust. Soc. Am. 65 (4) (1979) 943–950

    1979

  68. [76]

    Y.-J. Cha, A. Mostafavi, S. S. Benipal, Dnoisenet: Deep learning-based feedback active noise control in various noisy environments, Eng. Appl. Artif. Intel. 121 (2023) 105971. 30

    2023

  69. [77]

    S. J. Park, J. H. Yun, Y. C. Park, D. H. Youn, A delayless subband active noise control system for wideband noise control, IEEE Trans. Speech Audio Process. 9 (8) (2001) 892–899

    2001

  70. [78]

    P. N. Samarasinghe, W. Zhang, T. D. Abhayapala, Recent advances in active noise control inside automobile cabins: Toward quieter cars, IEEE Signal Process. Mag. 33 (6) (2016) 61–73. 31

    2016

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.