Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-10T15:58:58.879900Z
Paper Citation Record · LEDGER
Speech-preserving active noise control: a deep learning approach in reverberant environments
As of 10 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2604.10979.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
50 of 50 outbound references displayed
- verified exact2
- verified fuzzy46
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 14c6e2fd-aacc-44f4-b468-51794c31e173 · outbound
This paper cites an unresolved cited work.
Speech-preserving active noise control: a deep learning approach in reverberant environments Unresolved cited work
Reference 1
- Resolution
- unresolved
- raw_fallback, observed 2026-05-17T17:35:02.126048Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 14b8aadb-c4a7-47fb-96bd-935d123441fb · outbound
This paper cites Active noise control: a tutorial review.
Speech-preserving active noise control: a deep learning approach in reverberant environments Active noise control: a tutorial review
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.120562Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b37c4f4f-b93b-458c-a2b1-b08f0b614882 · outbound
This paper cites Deep ANC: A deep learning approach to active noise control.
Speech-preserving active noise control: a deep learning approach in reverberant environments Deep ANC: A deep learning approach to active noise control
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.123322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 7ef228d4-533d-413b-a0ea-6467932167c6 · outbound
This paper cites Pyroomacoustics: A Python package for audio room simulation and array processing algorithms.
Speech-preserving active noise control: a deep learning approach in reverberant environments Pyroomacoustics: A Python package for audio room simulation and array processing algorithms
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:01.998277Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 426ebbfd-d48e-4264-9e9e-fae369657dfb · outbound
This paper cites 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-preserving active noise control: a deep learning approach in reverberant environments Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recognition systems
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.023767Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation de813de5-ff18-4325-b5d7-73377d1715d6 · outbound
This paper cites Librispeech: An ASR corpus based on public domain audio books.
Speech-preserving active noise control: a deep learning approach in reverberant environments Librispeech: An ASR corpus based on public domain audio books
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:01.986608Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 4d1b3ce5-f756-4bfa-9833-99ffd4b0391d · outbound
This paper cites an unresolved cited work.
Speech-preserving active noise control: a deep learning approach in reverberant environments Unresolved cited work
Reference 7
- Resolution
- unresolved
- raw_fallback, observed 2026-05-17T17:35:02.003439Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 909e3323-940a-4e47-9dc6-40542ef957cb · outbound
This paper cites Hybrid FxRLS-FxNLMS Adaptive Algorithm for Active Noise Control in fMRI Application.
Speech-preserving active noise control: a deep learning approach in reverberant environments Hybrid FxRLS-FxNLMS Adaptive Algorithm for Active Noise Control in fMRI Application
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:01.995467Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 7eee76c6-3de4-459e-87b5-2cb0b6494eb5 · outbound
This paper cites A new structure for feedforward active noise control systems with improved online secondary path modeling.
Speech-preserving active noise control: a deep learning approach in reverberant environments A new structure for feedforward active noise control systems with improved online secondary path modeling
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.017918Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation fca12a44-4991-4751-8cf6-284ecaba233b · outbound
This paper cites Recent Advances in Active Noise Control Inside Automobile Cabins: Toward quieter cars.
Speech-preserving active noise control: a deep learning approach in reverberant environments Recent Advances in Active Noise Control Inside Automobile Cabins: Toward quieter cars
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.108409Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ff00f1d6-3eff-4e94-96f0-5cf19815986e · outbound
This paper cites Deep learning for audio signal processing.
Speech-preserving active noise control: a deep learning approach in reverberant environments Deep learning for audio signal processing
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:01.992542Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 1a0a8f24-639c-48c7-86cc-cb66fd388eb2 · outbound
This paper cites Comparison of neural network architectures for feedforward active control of nonlinear systems.
Speech-preserving active noise control: a deep learning approach in reverberant environments Comparison of neural network architectures for feedforward active control of nonlinear systems
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.006325Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 8a2b3bd7-421d-4064-b93a-0d882daf1cca · outbound
This paper cites Active noise reduction with filtered least-mean-square algorithm improved by long short-term memory models for radiation noise of diesel engine.
Speech-preserving active noise control: a deep learning approach in reverberant environments Active noise reduction with filtered least-mean-square algorithm improved by long short-term memory models for radiation noise of diesel engine
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.012057Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 936253b3-d28b-4c09-a9cf-b0222c47a95d · outbound
This paper cites Deep learning-based active noise control on construction sites.
Speech-preserving active noise control: a deep learning approach in reverberant environments Deep learning-based active noise control on construction sites
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.039964Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 95ceb3df-3921-4659-875a-354620475be8 · outbound
This paper cites Deep learning-based wind noise prediction study for automotive clay model.
Speech-preserving active noise control: a deep learning approach in reverberant environments Deep learning-based wind noise prediction study for automotive clay model
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.029302Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 7411a4c6-6cc7-44df-b812-f9563cb392e2 · outbound
This paper cites ELSTM-ANC-OSPM: Enhanced LSTM in active noise control systems with online secondary path modeling.
Speech-preserving active noise control: a deep learning approach in reverberant environments ELSTM-ANC-OSPM: Enhanced LSTM in active noise control systems with online secondary path modeling
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.037144Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 6e878556-d9d9-41cd-b798-edddc3937e6e · outbound
This paper cites A new hybrid adaptive self-loading FxLMS and CNN-GRU net for active time-varying noise control.
Speech-preserving active noise control: a deep learning approach in reverberant environments A new hybrid adaptive self-loading FxLMS and CNN-GRU net for active time-varying noise control
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.033298Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 7635903b-1118-446c-b921-77527f448d0f · outbound
This paper cites A new adaptive sound zone strategy-based hybrid FxNLMS and CNN-LSTM network for multichannel active noise control in a rehabilitation room.
Speech-preserving active noise control: a deep learning approach in reverberant environments A new adaptive sound zone strategy-based hybrid FxNLMS and CNN-LSTM network for multichannel active noise control in a rehabilitation room
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.057655Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 5692fc2a-6606-4a6c-963b-2e8978f06c6d · outbound
This paper cites CMANC Net: Fennec fox optimized CNN-BiLSTM network for real time and complex multitude active noise cancellation.
Speech-preserving active noise control: a deep learning approach in reverberant environments CMANC Net: Fennec fox optimized CNN-BiLSTM network for real time and complex multitude active noise cancellation
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.015018Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 26b8705a-e9d5-4e7c-a019-de87659a1ec3 · outbound
This paper cites Tan and D.
Speech-preserving active noise control: a deep learning approach in reverberant environments Tan and D
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.104978Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b2edbea0-6e0e-499e-8a2b-3a3dafdd3269 · outbound
This paper cites Zhang and D.
Speech-preserving active noise control: a deep learning approach in reverberant environments Zhang and D
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.000894Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 22ad4863-c7f6-462f-a542-a28bc4df6b45 · outbound
This paper cites Low-Latency Active Noise Control 85 Using Attentive Recurrent Network.
Speech-preserving active noise control: a deep learning approach in reverberant environments Low-Latency Active Noise Control 85 Using Attentive Recurrent Network
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.111494Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 01984e1a-ae62-4e6b-9862-0bdf1c6f3c1a · outbound
This paper cites HAD-ANC: A hybrid system comprising an adaptive filter and deep neural networks for active noise control.
Speech-preserving active noise control: a deep learning approach in reverberant environments HAD-ANC: A hybrid system comprising an adaptive filter and deep neural networks for active noise control
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.092017Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ccaf3d4a-4e04-4350-9fd9-0755a37a6dc4 · outbound
This paper cites Neural network-based ANC algorithms: a review.
Speech-preserving active noise control: a deep learning approach in reverberant environments Neural network-based ANC algorithms: a review
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.098528Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 61a5fb76-135f-4aed-acb7-c3f5d35bfe93 · outbound
This paper cites A comprehensive review on active noise reduction methods for aircraft aerodynamics system.
Speech-preserving active noise control: a deep learning approach in reverberant environments A comprehensive review on active noise reduction methods for aircraft aerodynamics system
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.088687Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation bd58c340-1eda-487c-878c-b308f7907488 · outbound
This paper cites A review of artificial intelligence-driven active vibration and noise control.
Speech-preserving active noise control: a deep learning approach in reverberant environments A review of artificial intelligence-driven active vibration and noise control
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.095329Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation f128f3cc-50a3-48be-8202-b1845a584878 · outbound
This paper cites Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation.
Speech-preserving active noise control: a deep learning approach in reverberant environments Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T09:31:04.430984Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation f5a9f42c-172b-4c00-b816-b09540796807 · outbound
This paper cites Deep Active Speech Cancellation with Mamba-Masking Network.
Speech-preserving active noise control: a deep learning approach in reverberant environments Deep Active Speech Cancellation with Mamba-Masking Network
Reference 28
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T09:31:04.438678Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 43ddd29f-893b-4ca2-a209-eaa0a1287b50 · outbound
This paper cites Image method for efficiently simulating small-room acoustics.
Speech-preserving active noise control: a deep learning approach in reverberant environments Image method for efficiently simulating small-room acoustics
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.085124Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 25608718-cba4-4100-a789-623c62b2d239 · outbound
This paper cites The performance of adaptive noise cancellation systems in reverberant rooms.
Speech-preserving active noise control: a deep learning approach in reverberant environments The performance of adaptive noise cancellation systems in reverberant rooms
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.066954Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 7a7cf2ea-5964-4acb-be4e-8cc5525c8f0c · outbound
This paper cites Directional selective fixed-filter active noise control based on a convolutional neural network in reverberant environments.
Speech-preserving active noise control: a deep learning approach in reverberant environments Directional selective fixed-filter active noise control based on a convolutional neural network in reverberant environments
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.077976Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 7836734a-8dce-488a-9ed0-fb5483cac93f · outbound
This paper cites Spatially selective active noise control systems.
Speech-preserving active noise control: a deep learning approach in reverberant environments Spatially selective active noise control systems
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.081331Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 7054f564-f95a-4d7b-a271-33c40b306a35 · outbound
This paper cites Complex spectral mapping for single- and multi-channel speech enhancement and robust ASR.
Speech-preserving active noise control: a deep learning approach in reverberant environments Complex spectral mapping for single- and multi-channel speech enhancement and robust ASR
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.101941Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 9dd271a1-edc7-4f7f-a171-044a90b81092 · outbound
This paper cites DCCRN: Deep complex convolution recurrent network for phase-aware speech enhancement.
Speech-preserving active noise control: a deep learning approach in reverberant environments DCCRN: Deep complex convolution recurrent network for phase-aware speech enhancement
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.114562Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation cd0b554b-9b44-40a9-a41c-a2846931a394 · outbound
This paper cites Complex spectral mapping with a convolutional recurrent network for monaural speech enhancement.
Speech-preserving active noise control: a deep learning approach in reverberant environments Complex spectral mapping with a convolutional recurrent network for monaural speech enhancement
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.054622Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a76af2a8-130b-44c2-8603-69bc02ce0ea2 · outbound
This paper cites Convolutional neural networks to enhance coded speech.
Speech-preserving active noise control: a deep learning approach in reverberant environments Convolutional neural networks to enhance coded speech
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.074438Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b0960d4c-2e6d-458b-a43b-c2888b7ed7e6 · outbound
This paper cites Fast and accurate deep 87 network learning by exponential linear units (ELUs).
Speech-preserving active noise control: a deep learning approach in reverberant environments Fast and accurate deep 87 network learning by exponential linear units (ELUs)
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:01.980431Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 82f77aa5-8b8d-4d9f-bed2-6cbfaa756585 · outbound
This paper cites U-Net: Convolutional networks for biomedical image segmentation.
Speech-preserving active noise control: a deep learning approach in reverberant environments U-Net: Convolutional networks for biomedical image segmentation
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.048977Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 03e92f28-5bb2-4331-8bf5-94fbdf61555b · outbound
This paper cites Adam: A method for stochastic optimization.
Speech-preserving active noise control: a deep learning approach in reverberant environments Adam: A method for stochastic optimization
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.051739Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b7aa2dfe-3e9e-4a7c-909f-bfec1982770e · outbound
This paper cites Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs.
Speech-preserving active noise control: a deep learning approach in reverberant environments Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.045957Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b995a87b-b9c8-4c50-b829-edf123a3409d · outbound
This paper cites An algorithm for intelligibility prediction of time – frequency weighted noisy speech.
Speech-preserving active noise control: a deep learning approach in reverberant environments An algorithm for intelligibility prediction of time – frequency weighted noisy speech
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.063529Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 0302f089-bb9b-4b3b-a455-fd52172b3d00 · outbound
This paper cites A hybrid SFANC-FxNLMS algorithm for active noise control based on deep learning.
Speech-preserving active noise control: a deep learning approach in reverberant environments A hybrid SFANC-FxNLMS algorithm for active noise control based on deep learning
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.117452Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation fc0e49c7-d44f-4ab1-bb12-5200add713ce · outbound
This paper cites Real-time implementation and 88 explainable AI analysis of delayless CNN-based selective fixed-filter active noise control.
Speech-preserving active noise control: a deep learning approach in reverberant environments Real-time implementation and 88 explainable AI analysis of delayless CNN-based selective fixed-filter active noise control
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.020880Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 9733e52b-3f8c-462c-8753-5edea0104745 · outbound
This paper cites Deep generative fixed-filter active noise control.
Speech-preserving active noise control: a deep learning approach in reverberant environments Deep generative fixed-filter active noise control
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.009152Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 21d103a4-f15c-431d-8d0c-caa81c2e402c · outbound
This paper cites Deep learning-based generative fixed-filter active noise control: Transferability and implementation.
Speech-preserving active noise control: a deep learning approach in reverberant environments Deep learning-based generative fixed-filter active noise control: Transferability and implementation
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.042952Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 889147fe-3629-47db-958c-969de601fcc5 · outbound
This paper cites GFANC-Kalman: Generative fixed-filter active noise control with CNN-Kalman filtering.
Speech-preserving active noise control: a deep learning approach in reverberant environments GFANC-Kalman: Generative fixed-filter active noise control with CNN-Kalman filtering
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.026546Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 1ff0e78c-a9db-4ccf-a6b5-6854ce24a148 · outbound
This paper cites Delayless generative fixed-filter active noise control based on deep learning and Bayesian filter.
Speech-preserving active noise control: a deep learning approach in reverberant environments Delayless generative fixed-filter active noise control based on deep learning and Bayesian filter
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:01.983460Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 02146234-a267-44a2-86ee-65382e80f299 · outbound
This paper cites Unsupervised learning based end-to-end delayless generative fixed-filter active noise control.
Speech-preserving active noise control: a deep learning approach in reverberant environments Unsupervised learning based end-to-end delayless generative fixed-filter active noise control
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.070602Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation bf3348cc-5786-4db1-8fd7-ad34746ec890 · outbound
This paper cites GFANC-RL: Reinforcement 89 learning-based generative fixed-filter active noise control.
Speech-preserving active noise control: a deep learning approach in reverberant environments GFANC-RL: Reinforcement 89 learning-based generative fixed-filter active noise control
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:02.060636Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation cf672b20-703c-41be-ac60-c335fc4eb99a · outbound
This paper cites WaveNet-Volterra Neural Network for Active Noise Control: A Fully Causal Approach.
Speech-preserving active noise control: a deep learning approach in reverberant environments WaveNet-Volterra Neural Network for Active Noise Control: A Fully Causal Approach
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T17:35:01.989765Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Pith citing papers
No inbound Pith citation observations are available.