Paper Citation Record · LEDGER

Evaluation of Deep Audio Representations for Hearables

As of 10 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2502.06664.

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

pith.paper-citation-record.v1
2502.06664 v2

Coverage vector

measured 40 of 40 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-08T14:47:08.014339Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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

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Reference resolution

40 of 40 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eb7c0932-bdf9-4b19-a40c-f0180cc9e3a8 · outbound

This paper cites World report on hearing,.

Evaluation of Deep Audio Representations for Hearables World report on hearing,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:09.104164Z

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Observation 553d561c-fbff-45f4-a85b-fbd72747b7e5 · outbound

This paper cites Hearing loss in adults,.

Evaluation of Deep Audio Representations for Hearables Hearing loss in adults,

Reference 2

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Observation af88bc24-03ab-422b-95bf-cee599bc7ef3 · outbound

This paper cites Hearing loss is associated with cortical thinning in cognitively normal older adults,.

Evaluation of Deep Audio Representations for Hearables Hearing loss is associated with cortical thinning in cognitively normal older adults,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:09.061665Z

Source-reported events for the cited work

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Observation ce4b6935-1c04-4235-9ddb-7680f4539520 · outbound

This paper cites Impairments in hearing and vision impact on mortality in older people: the ages-reykjavik study,.

Evaluation of Deep Audio Representations for Hearables Impairments in hearing and vision impact on mortality in older people: the ages-reykjavik study,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.999246Z

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Observation 33b0331e-4e10-4a7d-ace1-d83be13824a3 · outbound

This paper cites Us population data on hearing loss, trouble hearing, and hearing-device use in adults: National health and nutrition examination survey, 2011–12, 2015–16, and 2017–20,.

Evaluation of Deep Audio Representations for Hearables Us population data on hearing loss, trouble hearing, and hearing-device use in adults: National health and nutrition examination survey, 2011–12, 2015–16, and 2017–20,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.919605Z

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Observation d8d32833-2724-4be7-8f0c-3263a0e2b8bf · outbound

This paper cites Emerging technologies, market segments, and marketrak 10 insights in hearing health technology,.

Evaluation of Deep Audio Representations for Hearables Emerging technologies, market segments, and marketrak 10 insights in hearing health technology,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.862024Z

Source-reported events for the cited work

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Observation f3f0768c-e44d-469b-9569-81173ce224a3 · outbound

This paper cites Speech en- hancement for hearing-impaired listeners using deep neural networks with auditory-model based features,.

Evaluation of Deep Audio Representations for Hearables Speech en- hancement for hearing-impaired listeners using deep neural networks with auditory-model based features,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.846814Z

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Observation 656cb9f6-0cb0-4957-8af6-e82cdc535229 · outbound

This paper cites A deep learning based segregation algorithm to increase speech intelligibility for hearing- impaired listeners in reverberant-noisy conditions,.

Evaluation of Deep Audio Representations for Hearables A deep learning based segregation algorithm to increase speech intelligibility for hearing- impaired listeners in reverberant-noisy conditions,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.829702Z

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Observation 58cea415-be84-4440-bf3c-b841a426b359 · outbound

This paper cites An effectively causal deep learning algorithm to increase intelligibility in untrained noises for hearing-impaired listeners,.

Evaluation of Deep Audio Representations for Hearables An effectively causal deep learning algorithm to increase intelligibility in untrained noises for hearing-impaired listeners,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.814769Z

Source-reported events for the cited work

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Observation a75e408a-0b8c-48e8-84ff-70ad9ebb56ca · outbound

This paper cites Creating clarity in noisy environments by using deep learning in hearing aids,.

Evaluation of Deep Audio Representations for Hearables Creating clarity in noisy environments by using deep learning in hearing aids,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.800028Z

Source-reported events for the cited work

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Observation 3a34221f-396e-4b92-8160-c66c7dd8ac87 · outbound

This paper cites Audio self-supervised learning: A survey,.

Evaluation of Deep Audio Representations for Hearables Audio self-supervised learning: A survey,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.784631Z

Source-reported events for the cited work

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Observation 94aecc16-9ef4-440d-a189-b1bbc22ee8a9 · outbound

This paper cites Restoring speech intelligibility for hearing aid users with deep learn- ing,.

Evaluation of Deep Audio Representations for Hearables Restoring speech intelligibility for hearing aid users with deep learn- ing,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.768694Z

Source-reported events for the cited work

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Observation 59cb5849-b5da-470f-abfa-eb311fbd7e55 · outbound

This paper cites Towards learning a universal non- semantic representation of speech,.

Evaluation of Deep Audio Representations for Hearables Towards learning a universal non- semantic representation of speech,

Reference 13

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Observation 3fce7339-e3ed-4c88-bdcd-27f5c5bb74ce · outbound

This paper cites Lebench- mark: A reproducible framework for assessing self-supervised represen- tation learning from speech,.

Evaluation of Deep Audio Representations for Hearables Lebench- mark: A reproducible framework for assessing self-supervised represen- tation learning from speech,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.743558Z

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Observation 9e69218f-9667-458e-a338-3b0fd3b297b5 · outbound

This paper cites Superb: Speech processing universal performance benchmark,.

Evaluation of Deep Audio Representations for Hearables Superb: Speech processing universal performance benchmark,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.728542Z

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Observation 5cbb0603-3997-425d-b1a1-2a123390fd67 · outbound

This paper cites Towards learning universal audio representations,.

Evaluation of Deep Audio Representations for Hearables Towards learning universal audio representations,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.664027Z

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Observation d69b4a8e-3950-4884-b6dd-8aa18a408b79 · outbound

This paper cites Hear: Holistic evaluation of audio representations,.

Evaluation of Deep Audio Representations for Hearables Hear: Holistic evaluation of audio representations,

Reference 17

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Source-reported events for the cited work

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Observation c2cc9df0-0183-4736-9fe4-806966e77ed3 · outbound

This paper cites Decorrelating feature spaces for learning general-purpose audio representations,.

Evaluation of Deep Audio Representations for Hearables Decorrelating feature spaces for learning general-purpose audio representations,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.619070Z

Source-reported events for the cited work

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Observation 351f48fd-d978-4780-bccc-0683e9b0a9e6 · outbound

This paper cites Benchmarking Representations for Speech, Music, and Acoustic Events.

Evaluation of Deep Audio Representations for Hearables Benchmarking Representations for Speech, Music, and Acoustic Events

Reference 19

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Observation dbbc6c19-0531-485f-b36c-8eab7cdb5b92 · outbound

This paper cites Estimation of Room Acoustic Parameters: The ACE Challenge,.

Evaluation of Deep Audio Representations for Hearables Estimation of Room Acoustic Parameters: The ACE Challenge,

Reference 20

Resolution: verified exact; raw_fallback, observed 2026-08-08T14:47:08.355009Z

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Observation 9dd7637a-93aa-4fac-9988-9e5ca377b293 · outbound

This paper cites Reverberation effects on source localization and beamforming for hearing aid applications,.

Evaluation of Deep Audio Representations for Hearables Reverberation effects on source localization and beamforming for hearing aid applications,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.544109Z

Source-reported events for the cited work

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Observation 0c1903d0-52ad-49db-a8f5-1e99c9a59c3c · outbound

This paper cites Effects of room reflectance and background noise on perceived auditory distance,.

Evaluation of Deep Audio Representations for Hearables Effects of room reflectance and background noise on perceived auditory distance,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.529646Z

Source-reported events for the cited work

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Observation 193ea3ec-9d38-48f9-a06c-374a7d764dc5 · outbound

This paper cites Sound localization: Effects of reverberation time, speaker array, stimulus frequency, and stimulus rise/decay,.

Evaluation of Deep Audio Representations for Hearables Sound localization: Effects of reverberation time, speaker array, stimulus frequency, and stimulus rise/decay,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.513167Z

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Observation 5ffed7b1-3db3-493e-9729-69c4ff6e995e · outbound

This paper cites Effects of reverberation and noise on speech intelligibility in normal-hearing and aided hearing-impaired listeners,.

Evaluation of Deep Audio Representations for Hearables Effects of reverberation and noise on speech intelligibility in normal-hearing and aided hearing-impaired listeners,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.497358Z

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Observation da61f099-43c0-4391-b0e5-9c8fdf2484e3 · outbound

This paper cites Don't Make Your LLM an Evaluation Benchmark Cheater.

Evaluation of Deep Audio Representations for Hearables Don't Make Your LLM an Evaluation Benchmark Cheater

Reference 25

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Observation 63c0c753-8d14-4c60-869c-8631de2fe5a1 · outbound

This paper cites Tut database for acoustic scene classification and sound event detection,.

Evaluation of Deep Audio Representations for Hearables Tut database for acoustic scene classification and sound event detection,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.480367Z

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Observation a619abc2-d7e2-4039-b0d6-07dca95fd37b · outbound

This paper cites Libricount, a dataset for speaker count estimation,.

Evaluation of Deep Audio Representations for Hearables Libricount, a dataset for speaker count estimation,

Reference 27

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Observation 04eb0347-ce4d-4a45-bc65-750923d248f2 · outbound

This paper cites Librispeech: An ASR corpus based on public domain audio books,.

Evaluation of Deep Audio Representations for Hearables Librispeech: An ASR corpus based on public domain audio books,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.456289Z

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Observation 49ba8711-b16b-4477-87b6-976fc8a06409 · outbound

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

Evaluation of Deep Audio Representations for Hearables Audio set: An ontology and human- labeled dataset for audio events,

Reference 29

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Observation 5114e49c-5235-4498-8a70-cda1dcddc45c · outbound

This paper cites ESC: Dataset for environmental sound classification,.

Evaluation of Deep Audio Representations for Hearables ESC: Dataset for environmental sound classification,

Reference 30

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Observation 91661b0a-5914-49bc-b4df-b42075f6955e · outbound

This paper cites wav2vec 2.0: A framework for self-supervised learning of speech representations,.

Evaluation of Deep Audio Representations for Hearables wav2vec 2.0: A framework for self-supervised learning of speech representations,

Reference 31

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Observation 529fd8be-d432-4950-86e1-c6c24dd2e48a · outbound

This paper cites HuBERT: Self-Supervised Speech Representation Learn- ing by Masked Prediction of Hidden Units,.

Evaluation of Deep Audio Representations for Hearables HuBERT: Self-Supervised Speech Representation Learn- ing by Masked Prediction of Hidden Units,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.404983Z

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Observation 83f95f6a-ab70-4ee5-974c-ee653cdfd0fe · outbound

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

Evaluation of Deep Audio Representations for Hearables Wavlm: Large-scale self-supervised pre- training for full stack speech processing,

Reference 33

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Source-reported events for the cited work

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Observation 9ba18c47-ad51-489b-a2ad-5cbd56f2e8e3 · outbound

This paper cites BEATs: Audio Pre-Training with Acoustic Tokenizers.

Evaluation of Deep Audio Representations for Hearables BEATs: Audio Pre-Training with Acoustic Tokenizers

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-08T14:47:07.989640Z

Source-reported events for the cited work

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Observation 3e4e6b38-1910-4a57-a1ed-bb0b929aa685 · outbound

This paper cites wav2vec: Unsupervised Pre-training for Speech Recognition.

Evaluation of Deep Audio Representations for Hearables wav2vec: Unsupervised Pre-training for Speech Recognition

Reference 35

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Observation e289f3b5-e13e-4ee9-9c6e-94182ae90385 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Evaluation of Deep Audio Representations for Hearables BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 36

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Observation dc0f1a0c-da2c-42f6-9c52-6db19d93f9c0 · outbound

This paper cites GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio.

Evaluation of Deep Audio Representations for Hearables GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-08T14:47:08.004692Z

Source-reported events for the cited work

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Observation b8dc698b-945b-4e0b-bc4d-6653c2e744d6 · outbound

This paper cites VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation.

Evaluation of Deep Audio Representations for Hearables VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-08T14:47:08.009413Z

Source-reported events for the cited work

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Observation b1312020-5409-4e47-924f-0851590d980f · outbound

This paper cites Emerging properties in self-supervised vision transformers,.

Evaluation of Deep Audio Representations for Hearables Emerging properties in self-supervised vision transformers,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-08T14:47:08.014339Z

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Observation 9aedf2d6-e40c-4d9a-ba9d-3a8679eb6712 · outbound

This paper cites 3161–3165.

Evaluation of Deep Audio Representations for Hearables 3161–3165

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:47:08.710375Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.