Paper Citation Record · LEDGER

Towards Pre-training an Effective Respiratory Audio Foundation Model

As of 9 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 2 inbound Pith citation observations for arXiv:2505.15307.

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

pith.paper-citation-record.v1
2505.15307 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:24:02.955131Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:23:59.197904Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

62 of 62 outbound references displayed

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External citation measurements

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arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 142f7d73-06c6-4cb6-a4fb-2998a5425bf4 · outbound

This paper cites These models may serve as a fundamen- tal block for health monitoring and disease diagnosis applica- tions.

Towards Pre-training an Effective Respiratory Audio Foundation Model These models may serve as a fundamen- tal block for health monitoring and disease diagnosis applica- tions

Reference 1

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Towards Pre-training an Effective Respiratory Audio Foundation Model Unresolved cited work

Reference 2

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Towards Pre-training an Effective Respiratory Audio Foundation Model Unresolved cited work

Reference 3

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This paper cites Towards Pre-training an Effective Respiratory Audio Foundation Model.

Towards Pre-training an Effective Respiratory Audio Foundation Model Towards Pre-training an Effective Respiratory Audio Foundation Model

Reference 4

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Reference 5

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Towards Pre-training an Effective Respiratory Audio Foundation Model Unresolved cited work

Reference 6

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Reference 7

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Towards Pre-training an Effective Respiratory Audio Foundation Model Unresolved cited work

Reference 8

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Towards Pre-training an Effective Respiratory Audio Foundation Model Unresolved cited work

Reference 10

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Reference 11

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Reference 12

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Reference 14

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Reference 15

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Reference 23

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Observation 1ff53aec-7841-4745-8ac5-05e2f9716134 · outbound

This paper cites Bold results are better than the models above.

Towards Pre-training an Effective Respiratory Audio Foundation Model Bold results are better than the models above

Reference 24

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This paper cites We also evaluated the layer-wise performance (Figures 1 and 2).

Towards Pre-training an Effective Respiratory Audio Foundation Model We also evaluated the layer-wise performance (Figures 1 and 2)

Reference 25

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This paper cites For further pre-training experiments, we pre- trained the AudioSet-pre-trained M2D for 50 epochs on each Table 4: Data ablations of M2D on OPERA benchmark.

Towards Pre-training an Effective Respiratory Audio Foundation Model For further pre-training experiments, we pre- trained the AudioSet-pre-trained M2D for 50 epochs on each Table 4: Data ablations of M2D on OPERA benchmark

Reference 26

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Reference 27

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This paper cites Body acoustics for the non-invasive diagnosis of medical conditions,.

Towards Pre-training an Effective Respiratory Audio Foundation Model Body acoustics for the non-invasive diagnosis of medical conditions,

Reference 28

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This paper cites HeAR -- Health Acoustic Representations.

Towards Pre-training an Effective Respiratory Audio Foundation Model HeAR -- Health Acoustic Representations

Reference 29

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This paper cites Foun- dation models for cardiovascular disease detection via biosignals from digital stethoscopes,.

Towards Pre-training an Effective Respiratory Audio Foundation Model Foun- dation models for cardiovascular disease detection via biosignals from digital stethoscopes,

Reference 30

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This paper cites Towards open respiratory acoustic foundation models: Pretraining and benchmarking,.

Towards Pre-training an Effective Respiratory Audio Foundation Model Towards open respiratory acoustic foundation models: Pretraining and benchmarking,

Reference 31

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This paper cites A Generalist Audio Foundation Model for Comprehensive Body Sound Auscultation.

Towards Pre-training an Effective Respiratory Audio Foundation Model A Generalist Audio Foundation Model for Comprehensive Body Sound Auscultation

Reference 32

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Towards Pre-training an Effective Respiratory Audio Foundation Model The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms,

Reference 33

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This paper cites An open access database for the evaluation of respiratory sound classification algorithms,.

Towards Pre-training an Effective Respiratory Audio Foundation Model An open access database for the evaluation of respiratory sound classification algorithms,

Reference 34

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This paper cites Coswara: A respiratory sounds and symp- toms dataset for remote screening of sars-cov-2 infection,.

Towards Pre-training an Effective Respiratory Audio Foundation Model Coswara: A respiratory sounds and symp- toms dataset for remote screening of sars-cov-2 infection,

Reference 35

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This paper cites A dataset of lung sounds recorded from the chest wall using an elec- tronic stethoscope,.

Towards Pre-training an Effective Respiratory Audio Foundation Model A dataset of lung sounds recorded from the chest wall using an elec- tronic stethoscope,

Reference 36

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Towards Pre-training an Effective Respiratory Audio Foundation Model Mul- timedia respiratory database (respiratorydatabase@tr): Ausculta- tion sounds and chest x-rays,

Reference 37

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Towards Pre-training an Effective Respiratory Audio Foundation Model AST: Audio Spectrogram Transformer,

Reference 38

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This paper cites PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition,.

Towards Pre-training an Effective Respiratory Audio Foundation Model PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition,

Reference 39

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This paper cites HTS-AT: A hierarchical token-semantic audio transformer for sound classification and detection,.

Towards Pre-training an Effective Respiratory Audio Foundation Model HTS-AT: A hierarchical token-semantic audio transformer for sound classification and detection,

Reference 40

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

Towards Pre-training an Effective Respiratory Audio Foundation Model wav2vec 2.0: A framework for self-supervised learning of speech representa- tions,

Reference 41

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

Towards Pre-training an Effective Respiratory Audio Foundation Model HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units,

Reference 42

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Towards Pre-training an Effective Respiratory Audio Foundation Model WavLM: Large- Scale Self-Supervised Pre-Training for Full Stack Speech Pro- cessing,

Reference 43

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This paper cites Large-Scale Contrastive Language-Audio Pretrain- ing with Feature Fusion and Keyword-to-Caption Augmentation,.

Towards Pre-training an Effective Respiratory Audio Foundation Model Large-Scale Contrastive Language-Audio Pretrain- ing with Feature Fusion and Keyword-to-Caption Augmentation,

Reference 44

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Towards Pre-training an Effective Respiratory Audio Foundation Model CLAP: Learning Audio Concepts From Natural Language Supervision,

Reference 45

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Towards Pre-training an Effective Respiratory Audio Foundation Model Natural language su- pervision for general-purpose audio representations,

Reference 46

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Towards Pre-training an Effective Respiratory Audio Foundation Model BYOL for Audio: Exploring Pre-trained General-purpose Audio Representations,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:04.671201Z

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This paper cites Self-Supervised Audio Teacher- Student Transformer for Both Clip-Level and Frame-Level Tasks,.

Towards Pre-training an Effective Respiratory Audio Foundation Model Self-Supervised Audio Teacher- Student Transformer for Both Clip-Level and Frame-Level Tasks,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:04.562917Z

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Observation 5426603b-d7ea-4178-bc47-13f7f303b5be · outbound

This paper cites Masked autoencoders that lis- ten,.

Towards Pre-training an Effective Respiratory Audio Foundation Model Masked autoencoders that lis- ten,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:04.461628Z

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Observation 41bf64d3-34b6-488b-9fdc-7879a1bb3ef8 · outbound

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

Towards Pre-training an Effective Respiratory Audio Foundation Model BEATs: Audio Pre-Training with Acoustic Tokenizers,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:04.355731Z

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Observation 9f5a4285-1f0e-4e35-849d-206c308ff059 · outbound

This paper cites Masked Spectrogram Modeling using Masked Autoencoders for Learning General-purpose Audio Representation,.

Towards Pre-training an Effective Respiratory Audio Foundation Model Masked Spectrogram Modeling using Masked Autoencoders for Learning General-purpose Audio Representation,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:04.252111Z

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Observation 91c48e66-96e3-4e3d-8b7c-e0df7fff72ca · outbound

This paper cites Masked Modeling Duo: Towards a Universal Audio Pre- Training Framework,.

Towards Pre-training an Effective Respiratory Audio Foundation Model Masked Modeling Duo: Towards a Universal Audio Pre- Training Framework,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:04.132976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6c943c27-f57c-4cc4-a3a2-04183f87afe8 · outbound

This paper cites CED: Con- sistent ensemble distillation for audio tagging,.

Towards Pre-training an Effective Respiratory Audio Foundation Model CED: Con- sistent ensemble distillation for audio tagging,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:04.049288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 38bd9f8e-8263-4863-bd55-304842d7f60f · outbound

This paper cites Scaling up masked audio encoder learning for general audio clas- sification,.

Towards Pre-training an Effective Respiratory Audio Foundation Model Scaling up masked audio encoder learning for general audio clas- sification,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:03.900640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7a11206a-75f0-461d-9556-004bdddebbc3 · outbound

This paper cites Look, listen and learn more: Design choices for deep audio embeddings,.

Towards Pre-training an Effective Respiratory Audio Foundation Model Look, listen and learn more: Design choices for deep audio embeddings,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:03.765274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3d5616cf-bd18-4839-9cc1-c579123fb962 · outbound

This paper cites Masked autoencoders are scalable vision learners,.

Towards Pre-training an Effective Respiratory Audio Foundation Model Masked autoencoders are scalable vision learners,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:03.672826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c09f400b-d6b7-43db-8f18-2fc83ecf5e0e · outbound

This paper cites Lib- rispeech: An asr corpus based on public domain audio books,.

Towards Pre-training an Effective Respiratory Audio Foundation Model Lib- rispeech: An asr corpus based on public domain audio books,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T15:24:02.650062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cb9b7ef-09dd-4704-9f65-045ec712c5d1 · outbound

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

Towards Pre-training an Effective Respiratory Audio Foundation Model Audio Set: An ontology and human-labeled dataset for audio events,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:03.542692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 16b7dea5-d836-414e-8e80-bfa2a39475d3 · outbound

This paper cites A progressively expanded database for automated lung sound analysis: An update,.

Towards Pre-training an Effective Respiratory Audio Foundation Model A progressively expanded database for automated lung sound analysis: An update,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:03.449725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 39d9a490-f365-4eec-900f-97c0aaff6738 · outbound

This paper cites M2D-CLAP: Masked Modeling Duo Meets CLAP for Learning General-purpose Audio-Language Representation,.

Towards Pre-training an Effective Respiratory Audio Foundation Model M2D-CLAP: Masked Modeling Duo Meets CLAP for Learning General-purpose Audio-Language Representation,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T15:24:02.848288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0641af8-2e55-415d-a7f8-9e60599cbef3 · outbound

This paper cites Masked Modeling Duo for Speech: Specializing General- Purpose Audio Representation to Speech using Denoising Distil- lation,.

Towards Pre-training an Effective Respiratory Audio Foundation Model Masked Modeling Duo for Speech: Specializing General- Purpose Audio Representation to Speech using Denoising Distil- lation,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:03.347339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 46b05938-9b3e-4104-987c-a867c56a4b85 · outbound

This paper cites Composing General Audio Representation by Fusing Mul- tilayer Features of a Pre-trained Model,.

Towards Pre-training an Effective Respiratory Audio Foundation Model Composing General Audio Representation by Fusing Mul- tilayer Features of a Pre-trained Model,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:24:03.254839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation ed2134ba-1e56-4a08-9073-ef0b67ad81b1 · inbound