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Paper Citation Record · LEDGER

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling

As of 6 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2601.12222.

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

pith.paper-citation-record.v1
2601.12222 v2

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T09:53:18.015252Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T09:53:15.843405Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved25
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b7405806-77a9-40c0-9151-de434ba94455 · outbound

This paper cites This highlights the urgency for automated song aesthetics evaluation.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling This highlights the urgency for automated song aesthetics evaluation

Reference 1

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Observation 6b9eee91-095f-4f41-a56d-3f092280d4a2 · outbound

This paper cites an unresolved cited work.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Unresolved cited work

Reference 2

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source=pdf_text observed=2026-08-03T09:53:15.898451Z digest=sha256:d0ca9355be170e7f580bc46a27f478d48cb1751df6d534c04c28e390869c53c9

Observation 28e5def6-3a69-4942-955f-01106ae65b38 · outbound

This paper cites Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling

Reference 3

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source=pdf_text observed=2026-08-03T09:53:15.843405Z digest=sha256:503633afbc852b2f5c475a6b9b1196489ef7d906adb28f7e9bd1f0a059d36527

Observation 6cbd7d0f-c4a0-4891-99ff-df24604e2729 · outbound

This paper cites an unresolved cited work.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Unresolved cited work

Reference 4

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source=pdf_text observed=2026-08-03T09:53:15.987946Z digest=sha256:5d40598ede9858b1b62b701757f924f787520badfaf7d94fad1252054c38ecf0

Observation 702cce5f-3306-4178-9229-f9048c362e89 · outbound

This paper cites an unresolved cited work.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Unresolved cited work

Reference 5

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no resolver link, observed 2026-08-03T09:53:15.933102Z

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source=pdf_text observed=2026-08-03T09:53:15.933102Z digest=sha256:000f03cf7337e440ad6d517435663162392aad9346984c94c9eed2c3842082f1

Observation ea79b601-4054-4930-9b40-565d6bca1f48 · outbound

This paper cites Musiceval: A generative music dataset with expert rat- ings for automatic text-to-music evaluation,.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Musiceval: A generative music dataset with expert rat- ings for automatic text-to-music evaluation,

Reference 6

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source=pdf_text observed=2026-08-03T09:53:16.591925Z digest=sha256:7ec29343f0408a45497adce8f7a81702c76a497c4ff0cbd188b5bc28121a2c39

Observation b6bf4394-ebdf-4294-8c0e-85c4502f22c5 · outbound

This paper cites Nisqa: A deep cnn-self-attention model for multidimensional speech quality prediction with crowdsourced datasets,.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Nisqa: A deep cnn-self-attention model for multidimensional speech quality prediction with crowdsourced datasets,

Reference 7

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source=pdf_text observed=2026-08-03T09:53:16.089451Z digest=sha256:4fc0313bde85006b1b5732e35edaa9ff4ba755601c450d9fb9c9351487cb7d33

Observation 37942e44-ed09-4f36-9d98-dac668ad1b02 · outbound

This paper cites However, these studies mostly do not target full-length song aesthetics evaluation.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling However, these studies mostly do not target full-length song aesthetics evaluation

Reference 8

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source=pdf_text observed=2026-08-03T09:53:15.806089Z digest=sha256:2303ab36a777cebf8a04cbc308615f9ed02acd1769bca861b817e929d965922e

Observation 17f71b31-c5a4-4901-a927-9e71fd295ea9 · outbound

This paper cites Ldnet: Unified listener dependent mod- eling in mos prediction for synthetic speech,.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Ldnet: Unified listener dependent mod- eling in mos prediction for synthetic speech,

Reference 9

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source=pdf_text observed=2026-08-03T09:53:16.169145Z digest=sha256:57a8585baa1efaff8b86db6136890855aa9ce39035f04b5a95628994d9bb5cd5

Observation d4cfbd10-8a22-405e-9cf2-3efbae58ffa7 · outbound

This paper cites Utmos: Utokyo-sarulab system for voicemos challenge 2022,.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Utmos: Utokyo-sarulab system for voicemos challenge 2022,

Reference 10

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source=pdf_text observed=2026-08-03T09:53:16.236180Z digest=sha256:48a56f50ba4f2d732feab9e47815c6c2bdfd3a747cfe6271630f1b34bf492852

Observation 2b1d64be-1e71-42cd-8b71-bbffef20880d · outbound

This paper cites Pitch-and-spectrum-aware singing quality assessment with bias correction and model fu- sion,.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Pitch-and-spectrum-aware singing quality assessment with bias correction and model fu- sion,

Reference 11

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source=pdf_text observed=2026-08-03T09:53:16.299735Z digest=sha256:b46730d743a9ddb5e2547a59dd6e6d8e3fced64590a9c4a6a292418c2f2cffa0

Observation a10ba54a-1c1e-49ac-aae4-b5efe1f27d56 · outbound

This paper cites End- to-end automatic singing skill evaluation using cross- attention and data augmentation for solo singing and singing with accompaniment,.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling End- to-end automatic singing skill evaluation using cross- attention and data augmentation for solo singing and singing with accompaniment,

Reference 12

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source=pdf_text observed=2026-08-03T09:53:16.429516Z digest=sha256:784d51578c967f485af253344c9472bf771ee4a3ce722e1d36727e69bc0215cc

Observation 08935185-338f-4cbd-a06d-1242a073ac96 · outbound

This paper cites The AudioMOS Challenge 2025.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling The AudioMOS Challenge 2025

Reference 13

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source=pdf_text observed=2026-08-03T09:53:16.690620Z digest=sha256:eed2aa1967cfff171c0c3a042a3a46b9a65d77d3c2e2cff27cf294c24597ab92

Observation 3a901e94-5d4c-499a-b71d-94067222f9fe · outbound

This paper cites Mm- mos: Multi-domain multi-axis audio quality assess- ment,.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Mm- mos: Multi-domain multi-axis audio quality assess- ment,

Reference 14

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source=pdf_text observed=2026-08-03T09:53:16.750745Z digest=sha256:c541c2cfa6eab0456417943d64073943278039ce66fb0646e909d9e4e00ebe42

Observation c06d3fcd-d518-4eb5-bc67-a7ce980af0e0 · outbound

This paper cites Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound

Reference 15

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source=pdf_text observed=2026-08-03T09:53:16.809406Z digest=sha256:20608f4bb2edf5ed04bf4b9b6898460fdf3920a44b35c35c74d992a0d4f0572b

Observation 99d3aa96-020d-4d69-b566-d6b70846536d · outbound

This paper cites SongEval: A Benchmark Dataset for Song Aesthetics Evaluation.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling SongEval: A Benchmark Dataset for Song Aesthetics Evaluation

Reference 16

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source=pdf_text observed=2026-08-03T09:53:16.885407Z digest=sha256:7ef5cd0755e24ac07e1ee9bc9b6bc3121f3b2917fc8808bcc3d3cb73cb94f0f9

Observation 4653ee49-b08f-45d8-9268-1313cfc42ab7 · outbound

This paper cites an unresolved cited work.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Unresolved cited work

Reference 17

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source=pdf_text observed=2026-08-03T09:53:16.977985Z digest=sha256:03c19eac526b463a54d8f7f4d370305d92ab85f7004375bea72f898b071ff385

Observation 17484b94-1b38-4d80-bc7d-a3ef2264da95 · outbound

This paper cites MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector Quantization.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector Quantization

Reference 18

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source=pdf_text observed=2026-08-03T09:53:17.035715Z digest=sha256:3b7d39a17ca495c58ca1c54269de580f2cd55172632e7ec3efb21b57bb632927

Observation 93dcecb5-14d0-417f-b9e3-b4715020a88e · outbound

This paper cites Cbam: Convolutional block attention module,.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Cbam: Convolutional block attention module,

Reference 19

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source=pdf_text observed=2026-08-03T09:53:17.070696Z digest=sha256:9b02c7bfac46ea404f2b157d314e3024ba3311c354437ec2fb1eb6aa6deb56bb

Observation 78ea390a-8489-46ea-b467-1628e38268ce · outbound

This paper cites Attention is all you need,.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Attention is all you need,

Reference 20

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source=pdf_text observed=2026-08-03T09:53:17.140292Z digest=sha256:9d5e70ffbb099a8adb1bb51d00717956dcf4e2d08c0fceb9f4ab04e4cbfe4dae

Observation 6790b825-1cfc-4ff6-af6a-61e8e1407f45 · outbound

This paper cites Perceiving longer sequences with bi-directional cross- attention transformers,.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Perceiving longer sequences with bi-directional cross- attention transformers,

Reference 21

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source=pdf_text observed=2026-08-03T09:53:17.238396Z digest=sha256:af89b8b9ce5f65dcab59b754da37a364d539183db9e3a8e642b0d1f932def62c

Observation 6f43bb73-d39d-4a57-8256-ef87a8adb9e0 · outbound

This paper cites A hierarchical de- pression detection model based on vocal and emotional cues,.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling A hierarchical de- pression detection model based on vocal and emotional cues,

Reference 22

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Observation c466f080-c757-400d-9a82-d98609a7bc1d · outbound

This paper cites Generalization ability of mos predic- tion networks,.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Generalization ability of mos predic- tion networks,

Reference 23

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source=pdf_text observed=2026-08-03T09:53:17.519523Z digest=sha256:064427f9279d2c75574eb886719a1c4184459c60682fcacae56860e4b002c663

Observation 3f5fd9d6-7dab-4516-a51a-97895b31a49a · outbound

This paper cites The voicemos challenge 2022,.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling The voicemos challenge 2022,

Reference 24

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source=pdf_text observed=2026-08-03T09:53:17.705420Z digest=sha256:357ad879ee42c5cefea4a4aae88437eb9f40778538f7eab60055198ceb4df669

Observation c7fbf6b8-7ecb-4764-9616-b8ef4d518f91 · outbound

This paper cites KUIELab-MDX-Net: A Two-Stream Neural Network for Music Demixing.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling KUIELab-MDX-Net: A Two-Stream Neural Network for Music Demixing

Reference 25

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source=pdf_text observed=2026-08-03T09:53:17.817175Z digest=sha256:7970f91d77d70f0ca8a31dabc30cf392b645cdb839111bd489a7cc0cb8120ea8

Observation e783e582-dc98-4ab4-8397-0f925ded92d6 · outbound

This paper cites The voicemos challenge 2024: Beyond speech quality prediction,.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling The voicemos challenge 2024: Beyond speech quality prediction,

Reference 26

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source=pdf_text observed=2026-08-03T09:53:18.015252Z digest=sha256:c24f0ec23e48d2c7dd1e60fae7837f62f1e7b6bfa7a01a3177f137a6494d761e

Pith citing papers

Observation 28e5def6-3a69-4942-955f-01106ae65b38 · inbound

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling cites this paper.

Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling Song Aesthetics Evaluation with Multi-Stem Attention and Hierarchical Uncertainty Modeling

Reference 3

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