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Paper Citation Record · LEDGER

Watermarking Training Data of Music Generation Models

As of 13 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2412.08549.

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

pith.paper-citation-record.v1
2412.08549 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:49:42.644020Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

  • verified exact1
  • verified fuzzy11
  • unresolved20
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch1

External citation measurements

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Outbound references

Observation 6b4fc50c-148d-452d-bd59-0e2c70a3e2bc · outbound

This paper cites MusicLM: Generating Music From Text.

Watermarking Training Data of Music Generation Models MusicLM: Generating Music From Text

Reference 1

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source=pdf_text observed=2026-08-11T17:49:42.493048Z digest=sha256:ef840a067c08d93505f183113f363ad9ee45e42aea35683116b0dacb88cb3d87

Observation e77c646d-161e-4080-94c0-570f7c70ccd4 · outbound

This paper cites Exploring Musical Roots: Applying Audio Embeddings to Empower Influence Attribution for a Generative Music Model.

Watermarking Training Data of Music Generation Models Exploring Musical Roots: Applying Audio Embeddings to Empower Influence Attribution for a Generative Music Model

Reference 2

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source=pdf_text observed=2026-08-11T17:49:42.498822Z digest=sha256:abb044e21f43eb2f204160341d1820e467cd503c8b0fb01630d71544c5b3d357

Observation f55a1c4c-707f-48c2-a764-804c6f1d796e · outbound

This paper cites The Foundation Model Transparency Index.

Watermarking Training Data of Music Generation Models The Foundation Model Transparency Index

Reference 3

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source=pdf_text observed=2026-08-11T17:49:42.503886Z digest=sha256:267fe2a269fbba2dcb844a4de8a42e5ee75ce70b2394e05f12291f7a171094af

Observation 72974984-af44-4359-b1f4-081815b6dda0 · outbound

This paper cites Membership inference attacks from first principles.

Watermarking Training Data of Music Generation Models Membership inference attacks from first principles

Reference 4

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source=pdf_text observed=2026-08-11T17:49:42.508901Z digest=sha256:1f1a434cde1f9310399c230077d8c0e198bddb1ea2fd49324a25f4e583bd44d2

Observation d1aa6627-8508-4aa4-ba03-fab83f4d81c3 · outbound

This paper cites The secret sharer: Evaluating and testing unintended memorization in neural networks.

Watermarking Training Data of Music Generation Models The secret sharer: Evaluating and testing unintended memorization in neural networks

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T17:49:42.514050Z digest=sha256:5df850d17ea7821fb19cb424ebaadd86f3d2a600891ace363418ccf5a6ebe9c1

Observation d384d416-f36a-47f7-bafc-559860d3de46 · outbound

This paper cites Extracting training data from diffusion models.

Watermarking Training Data of Music Generation Models Extracting training data from diffusion models

Reference 6

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source=pdf_text observed=2026-08-11T17:49:42.518790Z digest=sha256:4be262117e3344a840ddd12d7f10f2eadbbea60b8fdfb1ee1cd9593a4fe3f738

Observation 74cc6489-2926-4a80-88a0-a7c43c7c6dfe · outbound

This paper cites WavMark: Watermarking for Audio Generation.

Watermarking Training Data of Music Generation Models WavMark: Watermarking for Audio Generation

Reference 7

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source=pdf_text observed=2026-08-11T17:49:42.523782Z digest=sha256:1fc8f2d804378a425ecd20fd80e3bc77368b3eab60c00e5ae2dcf97ab2ea6860

Observation 842e10d4-237e-4195-897f-144f26e8fafb · outbound

This paper cites Simple and controllable music generation.Advances in Neural Information Processing Systems, 36, 2024.

Watermarking Training Data of Music Generation Models Simple and controllable music generation.Advances in Neural Information Processing Systems, 36, 2024

Reference 8

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source=pdf_text observed=2026-08-11T17:49:42.528428Z digest=sha256:d9116da710860c332b764a2fff7c85a38cadde0d7e0114703a38b3d7e50b7b24

Observation cda9306e-a24b-479b-8603-ca2444a1d2ad · outbound

This paper cites drake” and “the weeknd.

Watermarking Training Data of Music Generation Models drake” and “the weeknd

Reference 9

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source=pdf_text observed=2026-08-11T17:49:42.532459Z digest=sha256:8900ca90a1e1c1df4c7d6eac2c6607a9480acabb02220c7aaa44564c3549972e

Observation 71efc51c-b708-43d3-8d2b-1343d35cf49e · outbound

This paper cites The Accuracy of Restricted Boltzmann Machine Models of Ising Systems.

Watermarking Training Data of Music Generation Models The Accuracy of Restricted Boltzmann Machine Models of Ising Systems

Reference 10

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local_arxiv, observed 2026-08-11T17:49:42.939012Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T17:49:42.537724Z digest=sha256:f01718c3e5c1c87026bcd9453a6abcc712e4618e00a0bc1c0f56d955a03b49e7

Observation 53bf9283-ae66-43c5-8a06-9fb49b60fac5 · outbound

This paper cites Blind Baselines Beat Membership Inference Attacks for Foundation Models.

Watermarking Training Data of Music Generation Models Blind Baselines Beat Membership Inference Attacks for Foundation Models

Reference 11

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source=pdf_text observed=2026-08-11T17:49:42.541917Z digest=sha256:de6034a07f804d7d12e79b0cf94adb36e4abeca69241227a68aed3aea3f19491

Observation 14e4452b-ea1c-45cc-beae-ca09d3d58b21 · outbound

This paper cites Oliveira, and Lei Li.

Watermarking Training Data of Music Generation Models Oliveira, and Lei Li

Reference 12

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T17:49:42.546100Z digest=sha256:06b549d205882e049a8cdfb63a5ea29101e505a918738a69cdd3787f6385c310

Observation 99fb7e17-010c-43dd-ad92-c7e33690bdcf · outbound

This paper cites High Fidelity Neural Audio Compression.

Watermarking Training Data of Music Generation Models High Fidelity Neural Audio Compression

Reference 13

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source=pdf_text observed=2026-08-11T17:49:42.550671Z digest=sha256:935c3d6c2e038ef1301c33e0a4d2d65bbe1a3d179e2c061ed86d392a390494cb

Observation 79c9ba49-fd6c-4aa0-9051-3c09f957c784 · outbound

This paper cites VampNet: Music Generation via Masked Acoustic Token Modeling.

Watermarking Training Data of Music Generation Models VampNet: Music Generation via Masked Acoustic Token Modeling

Reference 14

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source=pdf_text observed=2026-08-11T17:49:42.555192Z digest=sha256:c7f462d24c1ace37e818844295df4c79bed4fbc365a3c3b88a8de34cc58839eb

Observation c2c079d5-4f31-486e-a9cf-d31c5f05f356 · outbound

This paper cites Beyonce and adele publisher accuses firms of training ai on songs, 5 2024.

Watermarking Training Data of Music Generation Models Beyonce and adele publisher accuses firms of training ai on songs, 5 2024

Reference 15

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T17:49:42.559809Z digest=sha256:d470c3a47f0cfc9fe14309c0a82bef16a3d9aaef103d47af552001f72ab05b8f

Observation c60cd0b0-d033-4a6b-b28e-cf6e3479a24b · outbound

This paper cites Cnn architectures for large-scale audio classification.

Watermarking Training Data of Music Generation Models Cnn architectures for large-scale audio classification

Reference 16

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source=pdf_text observed=2026-08-11T17:49:42.564085Z digest=sha256:134959d099e02f0b7c21040a32694922953785dcf4f44eb0b2a3a17844e8adec

Observation 307249fa-1126-49ff-b5d9-98f6b22d93e6 · outbound

This paper cites Deduplicating training data mitigates privacy risks in language models.

Watermarking Training Data of Music Generation Models Deduplicating training data mitigates privacy risks in language models

Reference 17

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source=pdf_text observed=2026-08-11T17:49:42.568425Z digest=sha256:c9a32f34a1707bd087a65179aadd11a2d5e03ec0a6a221ebdcd61fa530763245

Observation 4715ead8-532b-4910-83b6-6a049752cac8 · outbound

This paper cites Fr\'echet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms.

Watermarking Training Data of Music Generation Models Fr\'echet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms

Reference 18

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source=pdf_text observed=2026-08-11T17:49:42.572599Z digest=sha256:a3ac9dce8442351c3aae056af1d6f91d27573c8d2e316938d43930c64ce712b3

Observation 2c8c330e-7ff4-43fc-ad6f-7aa86c472e58 · outbound

This paper cites Spread-spectrum watermarking of audio.

Watermarking Training Data of Music Generation Models Spread-spectrum watermarking of audio

Reference 19

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T17:49:42.577177Z digest=sha256:6f5d340c0af2b3c239885fd05279c5cfc2ea447e27ccfeb9e8edb10073e78aeb

Observation eef78128-14a6-4b2f-9f57-b24eae8f1c56 · outbound

This paper cites Us record labels sue ai music generators suno and udio for copyright infringement, 6 2024.

Watermarking Training Data of Music Generation Models Us record labels sue ai music generators suno and udio for copyright infringement, 6 2024

Reference 20

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T17:49:42.581647Z digest=sha256:35f7a9ab9e6364c70ac2446bf53c74770c6b8ddbb2bd1ed417d3f6a4b59acc95

Observation eb938b15-2c87-467d-814e-7b3490621349 · outbound

This paper cites High-Fidelity Audio Compression with Improved RVQGAN.

Watermarking Training Data of Music Generation Models High-Fidelity Audio Compression with Improved RVQGAN

Reference 21

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source=pdf_text observed=2026-08-11T17:49:42.586137Z digest=sha256:d4215c73a207311aa83985b0aa4effc6b91611a50359265efe7bc484bc1ed4f9

Observation 0af0fffd-21f7-489e-95d5-0a9d131b6fac · outbound

This paper cites Copyright traps for large language models.

Watermarking Training Data of Music Generation Models Copyright traps for large language models

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T17:49:42.590864Z digest=sha256:e5b43379c93c0566875928b8897d94d8640b5987aeecc239d60894d2690a6e01

Observation df123b63-996c-4378-a84c-7fb2e9685ec7 · outbound

This paper cites SoK: Membership Inference Attacks on LLMs are Rushing Nowhere (and How to Fix It).

Watermarking Training Data of Music Generation Models SoK: Membership Inference Attacks on LLMs are Rushing Nowhere (and How to Fix It)

Reference 23

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source=pdf_text observed=2026-08-11T17:49:42.595502Z digest=sha256:b3e4fcd2632dfdfb0dc7c16197492f76449d1142b72038507e719714aaad7504

Observation ccfeb941-96af-4616-8150-de5f2670dd91 · outbound

This paper cites An empirical analysis of memorization in fine-tuned autoregressive language models.

Watermarking Training Data of Music Generation Models An empirical analysis of memorization in fine-tuned autoregressive language models

Reference 24

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source=pdf_text observed=2026-08-11T17:49:42.600068Z digest=sha256:493d217eba0878a295ad81512d269bc2fc967bd2d830b8133f5380aa337b81fb

Observation 8fe65e3c-d85a-4ba1-924e-ae7707f3d4bb · outbound

This paper cites Scalable Extraction of Training Data from (Production) Language Models.

Watermarking Training Data of Music Generation Models Scalable Extraction of Training Data from (Production) Language Models

Reference 25

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source=pdf_text observed=2026-08-11T17:49:42.604514Z digest=sha256:a695f760845114d584d51b4206c27cecc37a6e01a8ee034b6f84db7c419f0114

Observation f123f29a-ae1c-4520-9dbe-625ab934b436 · outbound

This paper cites Neuroscience.

Watermarking Training Data of Music Generation Models Neuroscience

Reference 26

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T17:49:42.608890Z digest=sha256:8e48084711bd4411f3ede7ef53352e724d451ef495f0749284d659dc61b5f247

Observation b68656ff-c954-4223-885a-0974d9112536 · outbound

This paper cites Radioactive data: tracing through training.

Watermarking Training Data of Music Generation Models Radioactive data: tracing through training

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T17:49:42.613154Z digest=sha256:80c5234acacef08057d4e307a4ad575f6d05d77d0788a84af87c32b067dede6d

Observation a613d2c7-bd8d-4df8-b9ce-9f991142d1f6 · outbound

This paper cites Proactive detection of voice cloning with localized watermarking.

Watermarking Training Data of Music Generation Models Proactive detection of voice cloning with localized watermarking

Reference 28

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T17:49:42.617454Z digest=sha256:a996324952d8cca3b1e9bdc865838a6596e5bdd3d7453f82b911fd09bc5f5cbd

Observation 69c31db6-ee10-4f7f-85fd-38deee4f91d8 · outbound

This paper cites Membership inference attacks against machine learning models.

Watermarking Training Data of Music Generation Models Membership inference attacks against machine learning models

Reference 29

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T17:49:42.622035Z digest=sha256:ede07ea41f86fb762e56273f3a65b1cedb2dc167523469470d3e58c0e1c5a657

Observation 98925e5f-ef38-4f5a-b144-caa5fa8e11b8 · outbound

This paper cites Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models.

Watermarking Training Data of Music Generation Models Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models

Reference 30

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source=pdf_text observed=2026-08-11T17:49:42.626494Z digest=sha256:9413df724b38b5be464b94dc3f32173014e5705944f646bab75e2731717ce1aa

Observation 008dd644-b76e-4192-a6ea-30db9354e809 · outbound

This paper cites Understanding and Mitigating Copying in Diffusion Models.

Watermarking Training Data of Music Generation Models Understanding and Mitigating Copying in Diffusion Models

Reference 31

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source=pdf_text observed=2026-08-11T17:49:42.631137Z digest=sha256:92f0bff3cf47bfc3fd06397b9585815293d3da1342261265689c34a7c17c1af3

Observation f89619cb-0bd7-4fa8-a6fa-fc233740f2d9 · outbound

This paper cites Machine learning models that remember too much.

Watermarking Training Data of Music Generation Models Machine learning models that remember too much

Reference 32

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source=pdf_text observed=2026-08-11T17:49:42.636010Z digest=sha256:c8c25845f9086e00f5bb0a2e96d619679b7e69ae97db3a75d7a0325ccb376347

Observation 4f277eb8-4e0d-4b5d-8a75-36c50b8b621a · outbound

This paper cites DIAGNOSIS: Detecting Unauthorized Data Usages in Text-to-image Diffusion Models.

Watermarking Training Data of Music Generation Models DIAGNOSIS: Detecting Unauthorized Data Usages in Text-to-image Diffusion Models

Reference 33

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source=pdf_text observed=2026-08-11T17:49:42.639842Z digest=sha256:34acf0f1a9b74feaca20392daa403a7f052a330f8194e698bfa1c8688df235b9

Observation 3985e18b-1eb5-4a38-be99-f777f4bc8ed1 · outbound

This paper cites Enhanced membership inference attacks against machine learning models.

Watermarking Training Data of Music Generation Models Enhanced membership inference attacks against machine learning models

Reference 34

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raw_fallback, observed 2026-08-11T17:49:43.017701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T17:49:42.644020Z digest=sha256:d5290429f6faf816112c0951f7f031e7ed24e811cef0fb3fa1acf49fffb14cac

Pith citing papers

No inbound Pith citation observations are available.