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Paper Citation Record · LEDGER

Watermarking Training Data of Music Generation Models

As of 14 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 2026-08-14T06:32:32.682623+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

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Source: cited_works

Reference resolution

34 of 34 outbound references displayed

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  • 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:d0ba31c1fc11b87085863f9e05b1bbfe892da7175ea478c1858630ccf8f9cbea

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=sha256:9977f26693737c61c0f0fbcc900aba03a8a9e0fe5da65289c8a5b17165379a64

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:f76eaa7287e5909d19fd9bc9561789ec533e05ba8fbcfdc82f507e45dbd3d7a7

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:dd9125430d322cf44c1a83b72c14aee2573e87527d029099f7b31a684a2e1c92

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:49:42.514050Z digest=sha256:5f127bc6e528ff8b49ad25bb6bde4f5b3c9da7edfe82bb70035a29ec8ea2055f

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=sha256:52d116e13c02ced7bdddc29b018d9a0ec6d625b3e7fb8f311f61527340fb02a7

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:d5fab785a875c503058efef7b14da64f1bd9bd89f84545ba0fc0ef678acbc758

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=sha256:40e8b8f88c897e4492099d51db848890d8c26fc1426539cda0e6a3d87c65f9fd

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:a8bae512b0609644bd7ed2fca48d02cb778ba2804d9e558b5d7ea3daedc482ce

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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source=pdf_text observed=2026-08-11T17:49:42.537724Z digest=sha256:42099b62781d6877bc1d51fb41c1276d06713684b722abce51f5b3a3e0c3bb43

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:f493c012b001b80ae197ff66c3dceb294639ef21562a7556b00c78ac53acd2f0

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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source=pdf_text observed=2026-08-11T17:49:42.546100Z digest=sha256:118145633e2a43991b7bdcd35948eb1495f03ff92ae3e8c127a9644ad45042c1

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=sha256:9e5845ff7a4b3a0494e1501fb2db0708c8a6f7acb9295b824dda1b9846458902

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=sha256:4ce636f119e9cdc15403a2f1b54f06bebc2bb399ab7b2c7154d30ce9b16450f8

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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source=pdf_text observed=2026-08-11T17:49:42.559809Z digest=sha256:3d2998c2f2ea44a130eff773f5bc5e6f163c048ee83a32f8f7cd7562e98b8ac5

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=sha256:274ffce475ab31dc791b11c1dae1d893cd9317d5146be1a9baa94164871711c7

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=sha256:04a929c70786f8f8ca8bb162bc91d4c59b4a09251e6b0848c098221d54914c97

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=sha256:33755fcb71a692cf8aa47ade126f8ed869ace4f5289f81701558f2c07f508a85

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=pdf_text observed=2026-08-11T17:49:42.577177Z digest=sha256:e43ea732bd97d2fdec3f5278abd6fd14160c682e5e6aa332679a2e3f466bb7f1

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:49:42.581647Z digest=sha256:657122e4320f722a1abd9c1d66e8f9ddd6fe5b8779b84c0ba49fba871598e4b1

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=sha256:171c0bbcfa5804bf1fdae2722e08442397a41f8bb1edcf701844c3b641143b47

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:49:42.590864Z digest=sha256:261618be1924fc70b5fc3041c210b4e73313ac8512c40b1f8a6f278ab7620024

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:ed18eac44c871ba7eae1df56315bcf073a79a533bb7a4df697018ede6cd8b69a

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=sha256:27012da1d58ecc6a9b4277a0e03f2a88c07977f18b65b617b1ed155ee50c7977

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=sha256:97bdce4543e348117a1fc14405e4be6314a45ea8549109abfaf3723001ad4433

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:49:42.608890Z digest=sha256:500038dcf61e1691c6e4791ca66b023c47ee67843da0c342c193afb9da191c32

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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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:49:42.613154Z digest=sha256:16f27490849d1c4afa894dc2b9d16cc76200a49d51c220bd555273ddf331f22d

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:49:42.617454Z digest=sha256:7deb5326d0a543261dbf0bf9dc3e91ac5d177e46409fdffaf724c5e8b647fbaa

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=sha256:58e17f3d22979dad0f4a436075a20165e7d3b64fb0d6e1920b8832a5655fd0eb

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=sha256:2ae1f6f7ae407d5070dc2cfbb204b69c0c28f6da23b74064a9e074a4c11523ca

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:b6cecf96bf290b500f69e25f552cc6591eea6bc8fb5f77b265232386ce66cd22

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=sha256:62164515190c70f896fd23fefe7661ede41df2e8f92c5c2a339e0fc28eea071f

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=sha256:8055db30426dd25a557984d13ba84efe1ef1ac3b45002c1f973e56858cdb730d

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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verified fuzzy
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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:49:42.644020Z digest=sha256:b59381eca57ec58bb41f5dd3e58b74b043a30545bece38363931d6276de2cb89

Pith citing papers

No inbound Pith citation observations are available.