Paper Citation Record · LEDGER

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles

As of 8 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2505.22027.

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

pith.paper-citation-record.v1
2505.22027 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:21:04.819102Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:21:00.647688Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:21:05.009985Z

Reference resolution

38 of 38 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d7455c83-380f-41c7-9643-3f9da3e4ca85 · outbound

This paper cites Previous works primarily focused on CNN architectures [1–8], such as ResNet [9], EfficientNet [10], and CNN6 [11].

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Previous works primarily focused on CNN architectures [1–8], such as ResNet [9], EfficientNet [10], and CNN6 [11]

Reference 1

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Observation bd8787c3-dd17-42d4-99c1-9a8a5f108e99 · outbound

This paper cites Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles

Reference 2

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Observation 9e89f410-3943-48e0-a990-a0f4ce0102f3 · outbound

This paper cites The teacher ensemble creates soft labels by comput- ing the mean of the logits from all predictors, or by sampling the logits of a single predictor from the ensemble.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles The teacher ensemble creates soft labels by comput- ing the mean of the logits from all predictors, or by sampling the logits of a single predictor from the ensemble

Reference 3

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Observation 755ce1fe-2fc7-468b-b03c-ce39faec53e6 · outbound

This paper cites Main Results Table 1 presents the main results compared to previous RSC work on the ICBHI dataset.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Main Results Table 1 presents the main results compared to previous RSC work on the ICBHI dataset

Reference 4

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Observation f52c224e-0c4a-4533-9966-a8b4c8f26672 · outbound

This paper cites an unresolved cited work.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Unresolved cited work

Reference 5

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Observation 4d04307f-8d3e-4b04-a72c-c25600fa22e5 · outbound

This paper cites Our approach effectively transferred knowledge from the ensemble of teacher models to lightweight student models, achieving state-of-the-art performance on the ICBHI dataset.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Our approach effectively transferred knowledge from the ensemble of teacher models to lightweight student models, achieving state-of-the-art performance on the ICBHI dataset

Reference 6

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Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Unresolved cited work

Reference 7

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Observation f809423b-738d-438e-9742-ed7bb8645294 · outbound

This paper cites Adventitious respiratory classification using attentive residual neural networks,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Adventitious respiratory classification using attentive residual neural networks,

Reference 8

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Observation 509f22a2-681a-439e-8045-fa07dfc4abe5 · outbound

This paper cites Lungrn+ nl: An improved adventitious lung sound classification using non-local block resnet neural net- work with mixup data augmentation.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Lungrn+ nl: An improved adventitious lung sound classification using non-local block resnet neural net- work with mixup data augmentation

Reference 9

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Observation 99a41144-6de7-4b1d-abdf-e0d88e20bda4 · outbound

This paper cites Respirenet: A deep neural network for accurately detecting abnormal lung sounds in limited data setting,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Respirenet: A deep neural network for accurately detecting abnormal lung sounds in limited data setting,

Reference 10

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Observation b2c47a40-88d6-46f5-8393-878a26cc5563 · outbound

This paper cites Example-based Explanations with Adversarial Attacks for Res- piratory Sound Analysis,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Example-based Explanations with Adversarial Attacks for Res- piratory Sound Analysis,

Reference 11

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Observation 35930c9f-d195-47b1-84c4-03daad1ea44e · outbound

This paper cites Prototype learning for inter- pretable respiratory sound analysis,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Prototype learning for inter- pretable respiratory sound analysis,

Reference 12

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Observation 3b50d2a5-e1db-436e-bcec-81c94014ee74 · outbound

This paper cites A domain transfer based data augmenta- tion method for automated respiratory classification,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles A domain transfer based data augmenta- tion method for automated respiratory classification,

Reference 13

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Observation 87f2db7f-17f1-4ee1-aef8-8672047c7946 · outbound

This paper cites Lung sound classification using co-tuning and stochastic normalization,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Lung sound classification using co-tuning and stochastic normalization,

Reference 14

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Observation 6c42dd11-b5cb-48aa-8a5d-cbd580873cc7 · outbound

This paper cites Pretraining respiratory sound rep- resentations using metadata and contrastive learning,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Pretraining respiratory sound rep- resentations using metadata and contrastive learning,

Reference 15

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Observation 61071455-1d40-43b8-8b88-c49b2a3f8f0b · outbound

This paper cites Deep residual learning for image recognition,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Deep residual learning for image recognition,

Reference 16

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Observation 7343b8e5-21f7-42f6-bc9c-417b91fe7ab3 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Efficientnet: Rethinking model scaling for convolutional neural networks,

Reference 17

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Observation d01b04b9-ddb2-43b7-9b68-c62705668b0a · outbound

This paper cites Panns: Large-scale pretrained audio neural networks for audio pattern recognition,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Panns: Large-scale pretrained audio neural networks for audio pattern recognition,

Reference 18

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Observation f45328a7-84a5-440c-abf1-0696fcdcf1cc · outbound

This paper cites AST: Audio Spectrogram Transformer,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles AST: Audio Spectrogram Transformer,

Reference 19

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Observation 9a6ac5ad-5703-42b6-9384-6f37a74fab7f · outbound

This paper cites Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Clas- sification,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Clas- sification,

Reference 20

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Observation 2d8765c7-fe9f-46fe-bdfb-f7206dbb4a06 · outbound

This paper cites Im- agenet: A large-scale hierarchical image database,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Im- agenet: A large-scale hierarchical image database,

Reference 21

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Observation 50b7e820-0858-48a2-bbbc-c3a955d54a0f · outbound

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

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Audio set: An ontology and human-labeled dataset for audio events,

Reference 22

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Observation 6fc92291-310b-4809-ba98-518f2f006a4f · outbound

This paper cites Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance

Reference 23

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This paper cites Stethoscope-guided supervised contrastive learning for cross- domain adaptation on respiratory sound classification,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Stethoscope-guided supervised contrastive learning for cross- domain adaptation on respiratory sound classification,

Reference 24

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This paper cites Masked modeling duo: Towards a universal audio pre-training framework,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Masked modeling duo: Towards a universal audio pre-training framework,

Reference 25

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Observation 95630c39-d5ab-413f-80f2-c52953d516b8 · outbound

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

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Towards open respiratory acoustic foundation models: Pretraining and benchmarking,

Reference 26

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Observation 7a24a435-bbac-4669-8744-5dfaeff29cee · outbound

This paper cites Bts: Bridging text and sound modalities for metadata-aided res- piratory sound classification,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Bts: Bridging text and sound modalities for metadata-aided res- piratory sound classification,

Reference 27

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This paper cites A respiratory sound database for the development of auto- mated classification,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles A respiratory sound database for the development of auto- mated classification,

Reference 28

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This paper cites Towards inference efficient deep ensemble learning,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Towards inference efficient deep ensemble learning,

Reference 29

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This paper cites Distilling the Knowledge in a Neural Network.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Distilling the Knowledge in a Neural Network

Reference 30

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This paper cites Knowledge distilla- tion: A survey,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Knowledge distilla- tion: A survey,

Reference 31

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Observation db9bad85-e319-4fa8-b34f-d2f98c623135 · outbound

This paper cites Knowledge distillation and student- teacher learning for visual intelligence: A review and new out- looks,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Knowledge distillation and student- teacher learning for visual intelligence: A review and new out- looks,

Reference 32

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Observation 57e99ebf-815a-4dc7-978b-dc6d8fd618a7 · outbound

This paper cites Rethinking soft labels for knowledge distillation: A bias–variance tradeoff perspective,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Rethinking soft labels for knowledge distillation: A bias–variance tradeoff perspective,

Reference 33

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This paper cites From knowledge distillation to self-knowledge distillation: A unified approach with normalized loss and customized soft labels,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles From knowledge distillation to self-knowledge distillation: A unified approach with normalized loss and customized soft labels,

Reference 34

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Observation dfff7ebc-db84-4914-b3a8-c73ab38c162a · outbound

This paper cites Distillation Scaling Laws.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Distillation Scaling Laws

Reference 35

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Observation bbc9f4c9-e5ca-447d-a33e-152840f456b0 · outbound

This paper cites Repaugment: Input-agnostic representation-level augmentation for respiratory sound classification,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Repaugment: Input-agnostic representation-level augmentation for respiratory sound classification,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:21:05.283335Z

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This paper cites Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation,

Reference 37

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This paper cites Specaugment: A simple data augmentation method for automatic speech recognition,.

Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles Specaugment: A simple data augmentation method for automatic speech recognition,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T13:21:04.819102Z

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Pith citing papers

Observation bd8787c3-dd17-42d4-99c1-9a8a5f108e99 · inbound