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Paper Citation Record · LEDGER

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

As of 17 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 2026-08-17T06:30:58.91139+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:e014bae48cae86362844e2add8abf70e3cf994efec4355b29e83482c7c4a6dbd

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=sha256:7ef7a7aeb6cb13f2a82ec1675b715ff9413051a3efc5be1684e78b72f3215c51

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:a2171bd03405acfd15c9e23f332f3b688ee4f65ca4ce3fab22f9ce8fcd7b43d1

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=sha256:1e352bf85f3246045f1cf24e8c1df52fb18c66955fbd5442099d530ff5a518c2

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=sha256:4cb821497c31c645818d8d1a95af66d45b40568cda23911b868768e4ba197f65

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=sha256:06de4108928e0d61b1c036416f362165a876c9e857884f60319b2de5b9852a5a

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:c7c298b173b4380552617605dc28ab52ec6064d578ac3b6ce571bb3ac85abb3e

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=sha256:28e5170c3d3e8b28464042691dfa13855699dbcdf07a50a3cb3d1d968982929a

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=sha256:5ff7817a1e46dad4feb3a6c3719c28c08e4492c6938e33a7b9422cb12f8d85d6

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=sha256:2cc73aecf402a23d7ff11b42c7368aba92d2f5e5293e207415fd1ecaf89b14ec

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:cb522aad1b1549b3b0d306b21212b2b2192b8e144413dfacd57d00773bb0df32

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:e342d966bed81364ebcf481b6bdd4b303e6a4effecc40e4f6c47dfd72c42a023

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=sha256:1d875fbf2c92f498ef01caec99a0dcea368579d4baee98b4edf6d3191b76bfc4

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:b556995324558bce4fefdf095e2a8f0482a93d7ffbe6a6b53f5d44857abbe8ca

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=sha256:75a2beb143d84fc9a603d2d3e46b89eed39656c635c93e318015e7df37239848

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:d946669f2379bf9d75ce6794cce47542a84b04f7233c516b5ea7c0511bba222f

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:db7f20e119026716778f702d384e918339b86c91d53331e0adcf473083c76e90

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=sha256:218b8cf50e1efc6a517bc7cd6794c9dad0d3f7f0a94c4a03ea8397208671176d

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:dc0c92aed39e8e2ddceb254aba0a450d45d71767b7e12f42d96133eb12a1cb6c

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:efb191dfe3f67a49984347e7652853d3e90a0da3478a7da869e164ce2818d5f1

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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source=pdf_text observed=2026-08-03T09:53:17.401352Z digest=sha256:7cac45db9586e1a6dd1b172d7683c489f4b7473d5dc78872b708d756e981ee88

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:e374598d758d194055fa73786275ce3a81dc6dd08e0b5832e04a8dc76db1ea9d

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=sha256:8a0185439385213fd1ad6ec3c022684834209646e9e33648024f30b8715a1768

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:e5d6dd08fa81b74b257ac3a265c4d2613cd59d9f45e0b0e76dfcf6760aae2ce5

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=sha256:1f8cda5629c9983fe4ae99737d8e03b7412ad24ab953c631098fb0e55831bd77

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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source=pdf_text observed=2026-08-03T09:53:15.843405Z digest=sha256:7ef7a7aeb6cb13f2a82ec1675b715ff9413051a3efc5be1684e78b72f3215c51