Paper Citation Record · LEDGER

Automated data curation for self-supervised learning in underwater acoustic analysis

As of 9 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2505.20066.

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

pith.paper-citation-record.v1
2505.20066 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:05:27.811331Z

measured 21 of 21 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:05:25.953288Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:05:28.095603Z

Reference resolution

20 of 20 outbound references displayed

verified exact1
verified fuzzy15
unresolved3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 878b3bc4-331f-4e0e-a7b7-72bd3427e741 · outbound

This paper cites Automated data curation for self-supervised learning in underwater acoustic analysis.

Automated data curation for self-supervised learning in underwater acoustic analysis Automated data curation for self-supervised learning in underwater acoustic analysis

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T14:05:28.176092Z

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 80e56bce-43f4-40aa-867e-f1b30c254eb9 · outbound

This paper cites With no large, curated underwater acoustic dataset publicly avail- able yet, [7] and [8] proposed pretraining on AudioSet applying a mix-up strategy.

Automated data curation for self-supervised learning in underwater acoustic analysis With no large, curated underwater acoustic dataset publicly avail- able yet, [7] and [8] proposed pretraining on AudioSet applying a mix-up strategy

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:31.032053Z

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 68600fa1-8792-4a11-b58d-066ebe461e6a · outbound

This paper cites All these PAM audio recordings are combined into the result- ing dataset D.

Automated data curation for self-supervised learning in underwater acoustic analysis All these PAM audio recordings are combined into the result- ing dataset D

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:30.854866Z

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 3e3846f0-4c3a-47eb-a1fc-66d361338d20 · outbound

This paper cites Figure 3 illustrates the number of 10-second audio windows per individual ship, revealing a skewed distribution.

Automated data curation for self-supervised learning in underwater acoustic analysis Figure 3 illustrates the number of 10-second audio windows per individual ship, revealing a skewed distribution

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:30.600584Z

Source-reported events for the cited work

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Observation e73f8ed9-4171-4ade-ac16-3989f7cd56ef · outbound

This paper cites The study demonstrates that curation is a key aspect in extracting accurate SSL model representations from unlabeled un- derwater recordings.

Automated data curation for self-supervised learning in underwater acoustic analysis The study demonstrates that curation is a key aspect in extracting accurate SSL model representations from unlabeled un- derwater recordings

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:30.474473Z

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 4c8eaa93-7a3b-41fa-8fa3-01abaccca72d · outbound

This paper cites A survey on machine learning in ship radiated noise,.

Automated data curation for self-supervised learning in underwater acoustic analysis A survey on machine learning in ship radiated noise,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:30.235321Z

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 7afa78a8-4cd5-4a5d-9049-1d0d5adf7427 · outbound

This paper cites A survey on self-supervised learning: Algorithms, applications, and future trends,.

Automated data curation for self-supervised learning in underwater acoustic analysis A survey on self-supervised learning: Algorithms, applications, and future trends,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:30.072041Z

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 b6c97f4b-a9e7-4134-8e20-50ce3fca9263 · outbound

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

Automated data curation for self-supervised learning in underwater acoustic analysis Audio self-supervised learning: A survey,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:29.889669Z

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 cb2b8221-3e2b-4874-94ea-b06ab22b33b9 · outbound

This paper cites DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment.

Automated data curation for self-supervised learning in underwater acoustic analysis DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:26.794606Z

Source-reported events for the cited work

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Observation b239646b-b56e-4db2-b565-289c6db01055 · outbound

This paper cites Deepship: An underwater acoustic benchmark dataset and a separable convolution based autoencoder for classification,.

Automated data curation for self-supervised learning in underwater acoustic analysis Deepship: An underwater acoustic benchmark dataset and a separable convolution based autoencoder for classification,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:26.854212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e4e7ca5-8938-4f85-a3f7-5dd830c56216 · outbound

This paper cites Shipsear: An underwater vessel noise database,.

Automated data curation for self-supervised learning in underwater acoustic analysis Shipsear: An underwater vessel noise database,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:29.650270Z

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 61b58c10-e9a1-4365-9e2e-cfc7e7273fb2 · outbound

This paper cites Self-supervised learning–based under- water acoustical signal classification via mask mod- eling,.

Automated data curation for self-supervised learning in underwater acoustic analysis Self-supervised learning–based under- water acoustical signal classification via mask mod- eling,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:29.397617Z

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 b15c898e-b60f-42b7-b280-fb99e47f3502 · outbound

This paper cites Self-supervised learning-for un- derwater acoustic signal classification with mixup,.

Automated data curation for self-supervised learning in underwater acoustic analysis Self-supervised learning-for un- derwater acoustic signal classification with mixup,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:29.201052Z

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 2786701d-7357-4ab5-9649-37f785be9185 · outbound

This paper cites Masking hierarchical tokens for underwater acoustic target recognition with self-supervised learning,.

Automated data curation for self-supervised learning in underwater acoustic analysis Masking hierarchical tokens for underwater acoustic target recognition with self-supervised learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:28.995516Z

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 a78d5698-c9ad-4f81-827a-94b46a0d5df5 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision,.

Automated data curation for self-supervised learning in underwater acoustic analysis DINOv2: Learning Robust Visual Features without Supervision,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:28.857495Z

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 a6e74650-f0cf-4fe8-840c-c2ccaeaf9ef3 · outbound

This paper cites Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach.

Automated data curation for self-supervised learning in underwater acoustic analysis Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:27.452834Z

Source-reported events for the cited work

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Observation 749dd212-18d0-474e-94f9-5e52749a4541 · outbound

This paper cites Acav100m: Auto- matic curation of large-scale datasets for audio-visual video representation learning,.

Automated data curation for self-supervised learning in underwater acoustic analysis Acav100m: Auto- matic curation of large-scale datasets for audio-visual video representation learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:28.692535Z

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 0356db23-b761-47ad-9291-cfb9e4ddc1bc · outbound

This paper cites The computation of generalized embeddings for underwater acoustic target recogni- tion using contrastive learning,.

Automated data curation for self-supervised learning in underwater acoustic analysis The computation of generalized embeddings for underwater acoustic target recogni- tion using contrastive learning,

Reference 18

Resolution: verified exact; doi, observed 2026-08-07T14:05:27.980651Z

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 8e7e1729-836c-4656-ae2a-9918b7c33465 · outbound

This paper cites Web-scale k-means clustering,.

Automated data curation for self-supervised learning in underwater acoustic analysis Web-scale k-means clustering,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:28.493533Z

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 ea9bd29e-b5e4-4df8-b68d-45a760d454ee · outbound

This paper cites Data2vec: A general framework for self-supervised learning in speech, vision and lan- guage,.

Automated data curation for self-supervised learning in underwater acoustic analysis Data2vec: A general framework for self-supervised learning in speech, vision and lan- guage,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:28.343952Z

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 878b3bc4-331f-4e0e-a7b7-72bd3427e741 · inbound