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Paper Citation Record · LEDGER

Device Invariance using Domain Adaptation on Acoustic Scene Classification

As of 23 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2607.25887.

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

pith.paper-citation-record.v1
2607.25887 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T01:14:28.258445Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

24 of 24 outbound references displayed

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Outbound references

Observation bab92bb5-7d04-4f4d-ae6d-cd803f306275 · outbound

This paper cites Receptive Field Regu- larization Techniques for Audio Classification and Tagging With Deep Convolutional Neural Networks,.

Device Invariance using Domain Adaptation on Acoustic Scene Classification Receptive Field Regu- larization Techniques for Audio Classification and Tagging With Deep Convolutional Neural Networks,

Reference 1

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source=pdf_text observed=2026-08-01T01:14:24.817334Z digest=sha256:51bb7a243c6b6ae870191cbb1c10af82844fb8289dc42d6ff0e6fdcde9b307c2

Observation 6b48a8d6-d280-4826-b08c-41ce9ddbd313 · outbound

This paper cites Efficient Training of Audio Transformers with Patchout,.

Device Invariance using Domain Adaptation on Acoustic Scene Classification Efficient Training of Audio Transformers with Patchout,

Reference 2

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Observation 3202dcb8-5ab0-4adb-801b-468c0a49a2d4 · outbound

This paper cites CAA- Net: Conditional Atrous CNNs With Attention for Explainable Device- Robust Acoustic Scene Classification,.

Device Invariance using Domain Adaptation on Acoustic Scene Classification CAA- Net: Conditional Atrous CNNs With Attention for Explainable Device- Robust Acoustic Scene Classification,

Reference 3

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Observation 5106d7ec-9f93-413c-8645-6501d4470915 · outbound

This paper cites DCASENET: an integrated pretrained deep neural network for detecting and classifying acoustic scenes and events,.

Device Invariance using Domain Adaptation on Acoustic Scene Classification DCASENET: an integrated pretrained deep neural network for detecting and classifying acoustic scenes and events,

Reference 4

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source=pdf_text observed=2026-08-01T01:14:25.265978Z digest=sha256:08078c994898445ffa56daff2f49d23d9791fa1e8bb710c902dcc03193185c6b

Observation 4e3bde18-8995-4c83-9180-44262bcdebb5 · outbound

This paper cites Acoustic scene classification across cities and devices via feature disentanglement,.

Device Invariance using Domain Adaptation on Acoustic Scene Classification Acoustic scene classification across cities and devices via feature disentanglement,

Reference 5

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source=pdf_text observed=2026-08-01T01:14:25.430199Z digest=sha256:10291432ab53017c5968c566f29becbb24f5186f1bf975971ed7519f6d944f86

Observation 21353e19-f42f-414e-8ef2-6271a715e709 · outbound

This paper cites Unsupervised adversarial domain adaptation for acoustic scene classification.

Device Invariance using Domain Adaptation on Acoustic Scene Classification Unsupervised adversarial domain adaptation for acoustic scene classification

Reference 6

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source=pdf_text observed=2026-08-01T01:14:25.522828Z digest=sha256:60bf2b6469c910e1fea42755191d0c29ceec08b38d426ffc3e57cb1aecb27417

Observation 6fd8a2d0-4ad0-43eb-896d-ec80ecf0406a · outbound

This paper cites Unsupervised Adversarial Domain Adaptation Based on The Wasserstein Distance For Acoustic Scene Classification,.

Device Invariance using Domain Adaptation on Acoustic Scene Classification Unsupervised Adversarial Domain Adaptation Based on The Wasserstein Distance For Acoustic Scene Classification,

Reference 7

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source=pdf_text observed=2026-08-01T01:14:25.627577Z digest=sha256:efb488dc2d07f7969644e12f28990f59e1f909e0b21bfd64aa926f99278306c4

Observation 772252a2-4314-4002-a237-a7af8874ad97 · outbound

This paper cites Unsupervised multi-target domain adaptation for acoustic scene classification,.

Device Invariance using Domain Adaptation on Acoustic Scene Classification Unsupervised multi-target domain adaptation for acoustic scene classification,

Reference 8

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source=pdf_text observed=2026-08-01T01:14:25.740001Z digest=sha256:6d8e1e90964f2da9bc429910b050b0162a289056171c0b6b33c9d36cfde96922

Observation 5ce4b8fb-7274-4d7f-9894-b7a92c8e78f9 · outbound

This paper cites Acoustic Scene Classification with Mis- matched Devices Using CliqueNets and Mixup Data Augmentation,.

Device Invariance using Domain Adaptation on Acoustic Scene Classification Acoustic Scene Classification with Mis- matched Devices Using CliqueNets and Mixup Data Augmentation,

Reference 10

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source=pdf_text observed=2026-08-01T01:14:26.107419Z digest=sha256:a7ff17d5cf0695ee686d9ce0899e4c9604e09d47cf25c2cf60f509c69ce94efc

Observation 2e37716a-9121-400b-9b27-1e5fa08dee4d · outbound

This paper cites AST: Audio Spectrogram Transformer.

Device Invariance using Domain Adaptation on Acoustic Scene Classification AST: Audio Spectrogram Transformer

Reference 11

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Observation ec2ccee1-a9c6-4777-8311-14d4b28dc802 · outbound

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

Device Invariance using Domain Adaptation on Acoustic Scene Classification Masked Modeling Duo: towards a Universal Audio Pre-Training Framework,

Reference 12

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source=pdf_text observed=2026-08-01T01:14:26.408400Z digest=sha256:77cb2c4055e21bddc187255e30d8c2a654bcb917017b166b7a2fa9bc5b997809

Observation 095338e0-1747-46cf-a325-ef9b180d3f61 · outbound

This paper cites Look, Listen, and Learn More: Design Choices for Deep Audio Embeddings,.

Device Invariance using Domain Adaptation on Acoustic Scene Classification Look, Listen, and Learn More: Design Choices for Deep Audio Embeddings,

Reference 13

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Observation f9009b4f-d97b-45d0-9c63-4fcabcd727b7 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Device Invariance using Domain Adaptation on Acoustic Scene Classification An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

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Observation 82646a5c-e82d-41f8-80fe-d97a4dd00cf8 · outbound

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

Device Invariance using Domain Adaptation on Acoustic Scene Classification Audio Set: An ontology and human-labeled dataset for audio events,

Reference 15

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Observation 00a740b5-e878-4ea8-8198-55dc30eb18c1 · outbound

This paper cites Domain-Adversarial training of neural networks,.

Device Invariance using Domain Adaptation on Acoustic Scene Classification Domain-Adversarial training of neural networks,

Reference 16

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source=pdf_text observed=2026-08-01T01:14:26.944910Z digest=sha256:bc45e31869615e2fda90c588cb4b20cbbd90eae22ddc77fc53d0c19371dff0ec

Observation bd71a2bf-2e82-4b15-918d-def084bf8431 · outbound

This paper cites Gradually Vanishing Bridge for Adversarial Domain Adaptation.

Device Invariance using Domain Adaptation on Acoustic Scene Classification Gradually Vanishing Bridge for Adversarial Domain Adaptation

Reference 18

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Observation 10c4722f-99a2-49a8-9209-f58f26067ac7 · outbound

This paper cites Cycle Self-Training for Domain Adaptation.

Device Invariance using Domain Adaptation on Acoustic Scene Classification Cycle Self-Training for Domain Adaptation

Reference 19

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Observation 906e079e-b7a9-4541-8b73-55c99c5112ab · outbound

This paper cites On the impact of normalization strategies in unsupervised Adversarial Domain Adaptation for Acoustic Scene classification,.

Device Invariance using Domain Adaptation on Acoustic Scene Classification On the impact of normalization strategies in unsupervised Adversarial Domain Adaptation for Acoustic Scene classification,

Reference 20

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Observation 2af63dec-ba4f-45f2-8826-0600c670f609 · outbound

This paper cites Prototypical networks for domain adaptation in acoustic scene classification,.

Device Invariance using Domain Adaptation on Acoustic Scene Classification Prototypical networks for domain adaptation in acoustic scene classification,

Reference 21

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Observation 15ca22fe-774d-40dd-b97f-056f6ad6681b · outbound

This paper cites Conditional adversarial domain adaptation,.

Device Invariance using Domain Adaptation on Acoustic Scene Classification Conditional adversarial domain adaptation,

Reference 22

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Observation 3a3adc10-9cf2-48f3-94e6-208aff0f2efb · outbound

This paper cites Understanding the effective receptive field in deep convolutional neural networks,.

Device Invariance using Domain Adaptation on Acoustic Scene Classification Understanding the effective receptive field in deep convolutional neural networks,

Reference 23

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Observation 26b43847-39fe-420b-9854-dfeddc8810e6 · outbound

This paper cites Delving deep into the generalization of vision transformers under distribution shifts,.

Device Invariance using Domain Adaptation on Acoustic Scene Classification Delving deep into the generalization of vision transformers under distribution shifts,

Reference 24

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source=pdf_text observed=2026-08-01T01:14:28.258445Z digest=sha256:84ab12c6cc13130307b65405a19a69619e9cc42756cfc92e1624676f8c5f9866

Observation c9b5e0be-7c76-4d5a-98f6-4ed1c17df95b · outbound

This paper cites Adversarial Discriminative Domain Adaptation.

Device Invariance using Domain Adaptation on Acoustic Scene Classification Adversarial Discriminative Domain Adaptation

Reference 2017

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Observation 352941f4-9a15-4dba-b25e-30f28e793541 · outbound

This paper cites A Brief Review of Domain Adaptation.

Device Invariance using Domain Adaptation on Acoustic Scene Classification A Brief Review of Domain Adaptation

Reference 2020

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Pith citing papers

No inbound Pith citation observations are available.