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Paper Citation Record · LEDGER

NABEATs: Noise-Aware Audio Representation Learning

As of 14 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2607.16688.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.16688 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T20:16:26.731092Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T20:16:22.578854Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

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  • verified fuzzy0
  • unresolved50
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

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Outbound references

Observation 6530b898-6d16-4eb3-8309-12a06e4b38d5 · outbound

This paper cites These models learn general- purpose representations from large amounts of unlabeled audio data, enabling transfer to various downstream tasks.

NABEATs: Noise-Aware Audio Representation Learning These models learn general- purpose representations from large amounts of unlabeled audio data, enabling transfer to various downstream tasks

Reference 1

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Observation 0be39ce1-3592-4cd6-bb39-5d910c632907 · outbound

This paper cites NABEATs: Noise-Aware Audio Representation Learning.

NABEATs: Noise-Aware Audio Representation Learning NABEATs: Noise-Aware Audio Representation Learning

Reference 2

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Observation 935c3b38-bfd9-4cb7-a0ff-726ebf2e96a9 · outbound

This paper cites First, in Section 4, we evalu- ate performance on various downstream tasks under simulated noisy conditions using a custom-designed setup.

NABEATs: Noise-Aware Audio Representation Learning First, in Section 4, we evalu- ate performance on various downstream tasks under simulated noisy conditions using a custom-designed setup

Reference 3

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Observation 6ff14538-4508-4f18-b871-9b4f82f654c2 · outbound

This paper cites Setups We conducted evaluation on various downstream tasks according to [5, 6].

NABEATs: Noise-Aware Audio Representation Learning Setups We conducted evaluation on various downstream tasks according to [5, 6]

Reference 4

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Observation b28dcd8d-f6b9-4816-a677-ddaee13bce0a · outbound

This paper cites This dataset contains 15 machine types, each with 1000 normal training samples and 200 test samples including normal and anomalous samples.

NABEATs: Noise-Aware Audio Representation Learning This dataset contains 15 machine types, each with 1000 normal training samples and 200 test samples including normal and anomalous samples

Reference 5

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Observation 82b812f4-eb97-4733-b8ee-3c5bbdd16e91 · outbound

This paper cites an unresolved cited work.

NABEATs: Noise-Aware Audio Representation Learning Unresolved cited work

Reference 6

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Observation 89f60ad8-4659-458a-9554-95c7237c7e37 · outbound

This paper cites HuBERT: Self-supervised speech representation learning by masked prediction of hidden units,.

NABEATs: Noise-Aware Audio Representation Learning HuBERT: Self-supervised speech representation learning by masked prediction of hidden units,

Reference 7

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Observation 638d1d3a-18dd-4ddf-bb2e-8b69b9e00cc7 · outbound

This paper cites Wav2vec 2.0: A framework for self-supervised learning of speech representations,.

NABEATs: Noise-Aware Audio Representation Learning Wav2vec 2.0: A framework for self-supervised learning of speech representations,

Reference 8

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source=pdf_text observed=2026-08-01T20:16:23.060134Z digest=sha256:addebd4f2fde38315579eb8afc5cb81ab056d734be5afb2d45b2296b10555db4

Observation 0906312f-f869-4462-b093-95513daf5724 · outbound

This paper cites SUPERB: Speech Processing Universal PERfor- mance Benchmark,.

NABEATs: Noise-Aware Audio Representation Learning SUPERB: Speech Processing Universal PERfor- mance Benchmark,

Reference 9

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source=pdf_text observed=2026-08-01T20:16:23.144534Z digest=sha256:c6127698103716a5211c547476ba51373daeab8ac66f744dd07e3a052ac1c397

Observation f811b048-91f5-4d2e-af30-7a19d854438c · outbound

This paper cites BEATs: Audio pre-training with acoustic tokeniz- ers,.

NABEATs: Noise-Aware Audio Representation Learning BEATs: Audio pre-training with acoustic tokeniz- ers,

Reference 10

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source=pdf_text observed=2026-08-01T20:16:23.220858Z digest=sha256:f641b34dcf8ded1d30545fdaa451f4e06b15137d507604b62291562117a07e0b

Observation 638e6ad8-3499-4060-8411-3b0032341e3b · outbound

This paper cites BYOL for Audio: Exploring pre-trained general-purpose audio representations,.

NABEATs: Noise-Aware Audio Representation Learning BYOL for Audio: Exploring pre-trained general-purpose audio representations,

Reference 11

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source=pdf_text observed=2026-08-01T20:16:23.297273Z digest=sha256:0b4f9feadbe285a3d6816136c37a6a13ba0b09c113d9402de2eb3101770bb700

Observation da92d352-f22c-4973-bb0c-54875c2242c6 · outbound

This paper cites Masked Modeling Duo: Towards a universal audio pre-training framework,.

NABEATs: Noise-Aware Audio Representation Learning Masked Modeling Duo: Towards a universal audio pre-training framework,

Reference 12

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Observation 22e87656-fd49-488c-94ac-c72cafbdf840 · outbound

This paper cites SSAST: Self- supervised audio spectrogram transformer,.

NABEATs: Noise-Aware Audio Representation Learning SSAST: Self- supervised audio spectrogram transformer,

Reference 13

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source=pdf_text observed=2026-08-01T20:16:23.418413Z digest=sha256:e5d9a43e0578f78dbd27958a264287626b11db0205b740d2c3468322953da3d9

Observation 473254e8-748f-48c4-9400-fd0a1207a7bf · outbound

This paper cites Single channel target speaker extraction and recognition with speaker beam,.

NABEATs: Noise-Aware Audio Representation Learning Single channel target speaker extraction and recognition with speaker beam,

Reference 14

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source=pdf_text observed=2026-08-01T20:16:23.529959Z digest=sha256:c5590c0d4f2ea71ab123761472e676d0ef13c4e568eb00ac3d590c58e69c902d

Observation 4fc543ee-ac86-43d4-b6e5-01db2bdc1141 · outbound

This paper cites Separate anything you describe,.

NABEATs: Noise-Aware Audio Representation Learning Separate anything you describe,

Reference 15

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source=pdf_text observed=2026-08-01T20:16:23.654179Z digest=sha256:5dc420dac70d236ce60d4a9be3d60082d49a358ce383f9f72fc4a279c46212af

Observation fd9c3248-6acb-40dd-a147-738377383531 · outbound

This paper cites Self-guided target sound extraction and classification through universal sound separa- tion model and multiple clues,.

NABEATs: Noise-Aware Audio Representation Learning Self-guided target sound extraction and classification through universal sound separa- tion model and multiple clues,

Reference 16

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source=pdf_text observed=2026-08-01T20:16:23.749977Z digest=sha256:663c8b3506724360c7851ff582c959bc1eb127858b4e9586d9acaa8d6d5010ea

Observation 8414f13f-3882-4e25-8a77-433bc5fe6ba9 · outbound

This paper cites WavLM: Large-scale self-supervised pre-training for full stack speech processing,.

NABEATs: Noise-Aware Audio Representation Learning WavLM: Large-scale self-supervised pre-training for full stack speech processing,

Reference 17

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source=pdf_text observed=2026-08-01T20:16:23.865253Z digest=sha256:b132d1d2728e7d130d8e97787c8d62c2504cf91fea53e2a14b9580ec2e2d7769

Observation 92625cb3-882b-4a0e-a80a-2f20c4e77eeb · outbound

This paper cites Cocktail HuBERT: Generalized self-supervised pre-training for mixture and single-source speech,.

NABEATs: Noise-Aware Audio Representation Learning Cocktail HuBERT: Generalized self-supervised pre-training for mixture and single-source speech,

Reference 18

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source=pdf_text observed=2026-08-01T20:16:23.982545Z digest=sha256:7435a284361e43cf93dcecf723d1305533f9b5c9d4dc8eb0700a4ec9ade9f934

Observation 53564007-da13-4ab1-814c-9d56e5c799e3 · outbound

This paper cites An adapter based multi-label pre-training for speech separation and enhancement,.

NABEATs: Noise-Aware Audio Representation Learning An adapter based multi-label pre-training for speech separation and enhancement,

Reference 19

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source=pdf_text observed=2026-08-01T20:16:24.109982Z digest=sha256:4ee28002280a7dc9c53146a7797408065e22f7b6dbeadd530dcb8c4ee043f9f1

Observation 7962de11-ffca-4182-b497-e79338a576bc · outbound

This paper cites Weakly-Supervised Speech Pre-training: A Case Study on Target Speech Recognition,.

NABEATs: Noise-Aware Audio Representation Learning Weakly-Supervised Speech Pre-training: A Case Study on Target Speech Recognition,

Reference 20

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Observation 2f35ad3e-bcd4-46a0-a7ec-64e198324d65 · outbound

This paper cites Adapting self- supervised models to multi-talker speech recognition using speaker embeddings,.

NABEATs: Noise-Aware Audio Representation Learning Adapting self- supervised models to multi-talker speech recognition using speaker embeddings,

Reference 21

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Observation 4df20d3f-a0f0-48ad-8c2b-78536824ab73 · outbound

This paper cites SA-WavLM: Speaker-aware self-supervised pre-training for mixture speech,.

NABEATs: Noise-Aware Audio Representation Learning SA-WavLM: Speaker-aware self-supervised pre-training for mixture speech,

Reference 22

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Observation a9f753d3-9d21-4688-925a-e088aaf2a005 · outbound

This paper cites Suppression of acoustic noise in speech using spectral sub- traction,.

NABEATs: Noise-Aware Audio Representation Learning Suppression of acoustic noise in speech using spectral sub- traction,

Reference 23

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source=pdf_text observed=2026-08-01T20:16:24.540050Z digest=sha256:49a11a0bb19942af5b6125dff41eb892eafb009ac7f97408bc8d2276235916d0

Observation 7e04660b-3adc-45f5-990e-a09835d02177 · outbound

This paper cites Whisper-Flamingo: Integrating visual features into whisper for audio-visual speech recognition and translation,.

NABEATs: Noise-Aware Audio Representation Learning Whisper-Flamingo: Integrating visual features into whisper for audio-visual speech recognition and translation,

Reference 24

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Observation e2a33b81-bfb2-409a-a0fc-cca8e6075463 · outbound

This paper cites Attention is all you need,.

NABEATs: Noise-Aware Audio Representation Learning Attention is all you need,

Reference 25

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source=pdf_text observed=2026-08-01T20:16:24.774156Z digest=sha256:a5274b34c1e129d3a44cc903189020497c9c5061a04e11ce700f51b79887e40f

Observation 684dcb22-48ef-423c-a989-b35b2e66a150 · outbound

This paper cites Root mean square layer normalization,.

NABEATs: Noise-Aware Audio Representation Learning Root mean square layer normalization,

Reference 26

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Observation 797b2a49-c0ec-47c6-bd61-0a5981b9d04e · outbound

This paper cites GLU Variants Improve Transformer.

NABEATs: Noise-Aware Audio Representation Learning GLU Variants Improve Transformer

Reference 27

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Observation 35ccb02a-3cf8-4364-abbf-84a5c89773c0 · outbound

This paper cites FiLM: Visual reasoning with a general conditioning layer,.

NABEATs: Noise-Aware Audio Representation Learning FiLM: Visual reasoning with a general conditioning layer,

Reference 28

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Observation 99506ee8-102e-4ffa-8675-89454e635e5c · outbound

This paper cites SUNAC: Source-aware unified neural audio codec,.

NABEATs: Noise-Aware Audio Representation Learning SUNAC: Source-aware unified neural audio codec,

Reference 29

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Observation 386133aa-953b-4ece-a09d-1981d7a2c07d · outbound

This paper cites Description and discussion on DCASE 2025 chal- lenge task 2: First-shot unsupervised anomalous sound detection for machine condition monitoring,.

NABEATs: Noise-Aware Audio Representation Learning Description and discussion on DCASE 2025 chal- lenge task 2: First-shot unsupervised anomalous sound detection for machine condition monitoring,

Reference 30

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Observation 175b8a7d-c736-476b-95f7-69d1e95dd3e4 · outbound

This paper cites FSD50K: An open dataset of human-labeled sound events,.

NABEATs: Noise-Aware Audio Representation Learning FSD50K: An open dataset of human-labeled sound events,

Reference 31

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Observation ec52901c-1614-4503-a369-aa8de0670231 · outbound

This paper cites Audio set: An ontology and human-labeled dataset for audio events,.

NABEATs: Noise-Aware Audio Representation Learning Audio set: An ontology and human-labeled dataset for audio events,

Reference 32

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Observation e1aaecc4-d15c-4373-a38a-64062a8cc677 · outbound

This paper cites WHAM!: Extending speech separation to noisy environments,.

NABEATs: Noise-Aware Audio Representation Learning WHAM!: Extending speech separation to noisy environments,

Reference 33

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Observation e21b7d3c-55fa-47ff-bfff-e29ab653bb26 · outbound

This paper cites The Diverse Environments Multi-channel Acoustic Noise Database (DEMAND): A database of multichannel environmental noise recordings,.

NABEATs: Noise-Aware Audio Representation Learning The Diverse Environments Multi-channel Acoustic Noise Database (DEMAND): A database of multichannel environmental noise recordings,

Reference 34

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Observation f870dc26-7d5a-4684-a5da-f6d3c471408b · outbound

This paper cites The QUT-NOISE- TIMIT corpus for the evaluation of voice activity detection algo- rithms,.

NABEATs: Noise-Aware Audio Representation Learning The QUT-NOISE- TIMIT corpus for the evaluation of voice activity detection algo- rithms,

Reference 35

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Observation b0578b45-398a-485e-a73e-bcb5fa888252 · outbound

This paper cites A dataset and taxonomy for urban sound research,.

NABEATs: Noise-Aware Audio Representation Learning A dataset and taxonomy for urban sound research,

Reference 36

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Observation 0a4f97c0-848f-4068-a454-19adfcabb356 · outbound

This paper cites Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.

NABEATs: Noise-Aware Audio Representation Learning Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

Reference 37

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source=pdf_text observed=2026-08-01T20:16:25.952887Z digest=sha256:da2f841f31f5247ad0cfc72904cf571e2bb372e56f8afa703875fe05b66d5514

Observation 454d5976-327d-4713-82e7-2ba7b7b8071d · outbound

This paper cites CREMA-D: Crowd-sourced emotional multimodal actors dataset,.

NABEATs: Noise-Aware Audio Representation Learning CREMA-D: Crowd-sourced emotional multimodal actors dataset,

Reference 38

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source=pdf_text observed=2026-08-01T20:16:25.999729Z digest=sha256:d0d1906ef399aea9149b6fb66adb361cea769707e39b7f6c75a5d4fb204566de

Observation cfa18884-04fb-4f4f-91f2-69e659f4a039 · outbound

This paper cites Neural audio synthesis of musical notes with wavenet autoencoders,.

NABEATs: Noise-Aware Audio Representation Learning Neural audio synthesis of musical notes with wavenet autoencoders,

Reference 39

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source=pdf_text observed=2026-08-01T20:16:26.081750Z digest=sha256:d430b136dfa16c11230acaedc976a39c30f5b994434cf2e8ed57a6dfdcc1c817

Observation 48c4ffd4-02e1-4edc-93bf-abdd3859a882 · outbound

This paper cites One billion audio sounds from GPU-enabled modular synthesis,.

NABEATs: Noise-Aware Audio Representation Learning One billion audio sounds from GPU-enabled modular synthesis,

Reference 40

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source=pdf_text observed=2026-08-01T20:16:26.145112Z digest=sha256:7004e0535d1e5c93600b1b813e315cb9e1014fcd311cabd674c3664a695a95ea

Observation 899ef544-ad3e-4808-a9cf-5acbcef71c54 · outbound

This paper cites The third ‘CHiME’ speech separation and recognition challenge: Dataset, task and baselines,.

NABEATs: Noise-Aware Audio Representation Learning The third ‘CHiME’ speech separation and recognition challenge: Dataset, task and baselines,

Reference 41

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source=pdf_text observed=2026-08-01T20:16:26.198595Z digest=sha256:9903e145149c94f66537a357b48894a2721d472a8ce46c1d710bd9cf7e7f26b7

Observation 05c7c3ae-09a6-4438-a388-6ed18b0797c4 · outbound

This paper cites Rafii, A.

NABEATs: Noise-Aware Audio Representation Learning Rafii, A

Reference 42

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source=pdf_text observed=2026-08-01T20:16:26.251084Z digest=sha256:4af71d790efd4f6195b5a8793bdc492a421fb8f3f4607841384787bd6f70abfb

Observation 4b7417d5-3387-4413-9e4a-422959c8060f · outbound

This paper cites Visualizing data using t-SNE,.

NABEATs: Noise-Aware Audio Representation Learning Visualizing data using t-SNE,

Reference 43

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source=pdf_text observed=2026-08-01T20:16:26.303501Z digest=sha256:bfa736343d98075629b58d16deb3a7001a04b3d62bfca8513a84ca63ce75a270

Observation ba6fbb41-9b97-4510-bb77-edb849c13772 · outbound

This paper cites ToyADMOS2: Another dataset of miniature-machine operat- ing sounds for anomalous sound detection under domain shift condi- tions,.

NABEATs: Noise-Aware Audio Representation Learning ToyADMOS2: Another dataset of miniature-machine operat- ing sounds for anomalous sound detection under domain shift condi- tions,

Reference 44

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source=pdf_text observed=2026-08-01T20:16:26.352977Z digest=sha256:ae0ada6310ee868b523680ac05f62b9525c6ace08544bea5d11116853640f588

Observation f3bdad16-a684-4fa9-8c01-cd33190ecc08 · outbound

This paper cites MIMII DG: Sound dataset for mal- functioning industrial machine investigation and inspection for do- main generalization task,.

NABEATs: Noise-Aware Audio Representation Learning MIMII DG: Sound dataset for mal- functioning industrial machine investigation and inspection for do- main generalization task,

Reference 45

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source=pdf_text observed=2026-08-01T20:16:26.408995Z digest=sha256:023a399542fa65f5189dff6db28a9bed4a9c9db50c9535edc924731f5600fd88

Observation 0d60689f-516b-4cef-a353-442ff695107a · outbound

This paper cites Toy- ADMOS2025: The evaluation dataset for the DCASE2025T2 first- shot unsupervised anomalous sound detection for machine condition monitoring,.

NABEATs: Noise-Aware Audio Representation Learning Toy- ADMOS2025: The evaluation dataset for the DCASE2025T2 first- shot unsupervised anomalous sound detection for machine condition monitoring,

Reference 46

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source=pdf_text observed=2026-08-01T20:16:26.507632Z digest=sha256:d9d332d96af84d772af361ff70a1c3d49f03a29f876446da6f2f78329d6602a4

Observation a47bee83-edb3-4d6d-9150-a47a0726d828 · outbound

This paper cites Deep generic representations for domain-generalized anomalous sound detection,.

NABEATs: Noise-Aware Audio Representation Learning Deep generic representations for domain-generalized anomalous sound detection,

Reference 47

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source=pdf_text observed=2026-08-01T20:16:26.571209Z digest=sha256:f1e378960d4f260fd21c06099cef8d0b0de2b0ec646e0d71cb971aeefc5c2b40

Observation b78dd777-191f-4350-a701-6aac6c0cd90b · outbound

This paper cites SMOTE: Synthetic minority over-sampling technique,.

NABEATs: Noise-Aware Audio Representation Learning SMOTE: Synthetic minority over-sampling technique,

Reference 48

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source=pdf_text observed=2026-08-01T20:16:26.619882Z digest=sha256:44dfffd6f2eca4af2e8e310a404dfab60caf583380ea127a663aeaba1cac8414

Observation b5a773c7-0c7e-4d69-a048-c8bd5fb4e574 · outbound

This paper cites ASDKit: A toolkit for comprehensive evaluation of anomalous sound detection methods,.

NABEATs: Noise-Aware Audio Representation Learning ASDKit: A toolkit for comprehensive evaluation of anomalous sound detection methods,

Reference 49

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source=pdf_text observed=2026-08-01T20:16:26.680751Z digest=sha256:3a19319fb5337f5f0c8acc300a54891344d355bf65dc684f5d0655131e890d20

Observation 5682ae64-41a5-446d-bc9a-2bad735475e1 · outbound

This paper cites Adaptive prototype learning for anomalous sound detection with partially known attributes,.

NABEATs: Noise-Aware Audio Representation Learning Adaptive prototype learning for anomalous sound detection with partially known attributes,

Reference 50

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Pith citing papers

Observation 0be39ce1-3592-4cd6-bb39-5d910c632907 · inbound

NABEATs: Noise-Aware Audio Representation Learning cites this paper.

NABEATs: Noise-Aware Audio Representation Learning NABEATs: Noise-Aware Audio Representation Learning

Reference 2

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source=pdf_text observed=2026-08-01T20:16:22.578854Z digest=sha256:989ba55d6dc3faba98b2c4b54242a19c5aa9b7cde8f286e47c6fc6ad6da50eae