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Paper Citation Record · LEDGER

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation

As of 18 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2607.13903.

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

pith.paper-citation-record.v1
2607.13903 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T03:25:50.306840Z

measured 30 of 30 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 2026-08-02T03:25:50.204971Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

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External citation measurements

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Outbound references

Observation 59db3020-5b1a-4ea4-9bc9-f77832a7e587 · outbound

This paper cites Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation

Reference 1

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Observation b8fed224-5ba3-4bec-9e1d-5aa4283afa8b · outbound

This paper cites Ideally, aesthetic judgments should depend on genre only through quality-relevant musical factors.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Ideally, aesthetic judgments should depend on genre only through quality-relevant musical factors

Reference 2

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Observation 8e735f37-c9ec-4473-80ef-3066ce87142f · outbound

This paper cites an unresolved cited work.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Unresolved cited work

Reference 3

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Observation 966162cf-da94-469d-8041-f61a4efde63f · outbound

This paper cites Instead of relying purely on intrin- sic acoustic cues relevant to musical aesthetics, the model tends to exploit correlations between genre-related features and score.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Instead of relying purely on intrin- sic acoustic cues relevant to musical aesthetics, the model tends to exploit correlations between genre-related features and score

Reference 4

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Observation 8d417c11-b649-4896-9ae2-0b8fd5dc66d3 · outbound

This paper cites Following the standard SongEval setup, the baseline model is trained us- ing the MSE objective with the Adam optimizer, a learn- ing rate of3×10 −5, for 30 epochs.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Following the standard SongEval setup, the baseline model is trained us- ing the MSE objective with the Adam optimizer, a learn- ing rate of3×10 −5, for 30 epochs

Reference 5

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Observation bff6cce8-db40-4db7-a51c-a10f504cfd28 · outbound

This paper cites Through a pro- gressive diagnostic analyses, we show that models rely on genre-related signals as proxies for musical aesthetics, fur- ther leading to pop-centric bias.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Through a pro- gressive diagnostic analyses, we show that models rely on genre-related signals as proxies for musical aesthetics, fur- ther leading to pop-centric bias

Reference 6

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Observation 9cd5093e-e1b5-47c5-abb8-c76c1e7e303d · outbound

This paper cites While our method mitigates shortcut learning dur- ing optimization, a more diverse dataset with reliable and balanced annotations remains essential.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation While our method mitigates shortcut learning dur- ing optimization, a more diverse dataset with reliable and balanced annotations remains essential

Reference 7

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source=pdf_text observed=2026-08-02T03:25:50.228710Z digest=sha256:c56bc4045c4a231ccadde6069a9dcb9d56e8ae42aa28124fdf95bdac6b0e7956

Observation bceef128-e02e-480d-ad8b-ea0179ed1851 · outbound

This paper cites Survey on the evaluation of generative models in music,.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Survey on the evaluation of generative models in music,

Reference 8

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source=pdf_text observed=2026-08-02T03:25:50.232176Z digest=sha256:ab760a6924a8eaaea1b22aa33f8dbd6fe32cc840d23236b753f288f6770fa527

Observation 50a2b033-8ad3-4448-b231-f13527c686e4 · outbound

This paper cites Benchmarking music generation models and metrics via human preference studies,.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Benchmarking music generation models and metrics via human preference studies,

Reference 9

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source=pdf_text observed=2026-08-02T03:25:50.235616Z digest=sha256:7b5180f270a8e6b3562da3b181338e23f3abee926c49b1ff4e788fe6364e3ef0

Observation 66f4edb7-28d8-4508-8d13-e04b16b9dab7 · outbound

This paper cites Musicrl: aligning Proceedings of the 27th ISMIR Conference, Abu Dhabi, UAE, November 08–12, 2026 music generation to human preferences,.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Musicrl: aligning Proceedings of the 27th ISMIR Conference, Abu Dhabi, UAE, November 08–12, 2026 music generation to human preferences,

Reference 10

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source=pdf_text observed=2026-08-02T03:25:50.239431Z digest=sha256:78803b7f066336f5222496b2fd8781477596e50fee82917258ec6c4ad62a3bb8

Observation 587e34ae-1892-4534-877b-1f7ed9f78496 · outbound

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

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound

Reference 11

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source=pdf_text observed=2026-08-02T03:25:50.242720Z digest=sha256:15d608fbf963da0a499c4fd61d44239f79188c6472a51765e64321e4ad4e0558

Observation 27e2d958-5223-438d-9f00-2a0a13e1222f · outbound

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

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation SongEval: A Benchmark Dataset for Song Aesthetics Evaluation

Reference 12

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source=pdf_text observed=2026-08-02T03:25:50.246420Z digest=sha256:4ea800958ab28b787bc59f75333b0e5a12aae29ce22fa5f4c63fb78628ca5a90

Observation c57be581-8325-4b42-8ac2-2f27482d9289 · outbound

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

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Musiceval: A generative music dataset with expert ratings for automatic text-to- music evaluation,

Reference 13

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source=pdf_text observed=2026-08-02T03:25:50.250189Z digest=sha256:f3b5a2f4b9d1db7b78a998825b919b83872fc02e58a742f972ae1961093186cd

Observation 447e2b35-4d6a-4d56-a22b-28261ef656c9 · outbound

This paper cites Fr\’echet audio distance: A reference-free metric for evaluating music enhancement algorithms,.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Fr\’echet audio distance: A reference-free metric for evaluating music enhancement algorithms,

Reference 14

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source=pdf_text observed=2026-08-02T03:25:50.253763Z digest=sha256:3d7e9eaccc4a59e31d988ae4e911dbd89c89afb0907673239012ed3b57223622

Observation 97a796b2-b450-4f09-be38-0f38369e694d · outbound

This paper cites Mulan: A joint embedding of music audio and natural language,.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Mulan: A joint embedding of music audio and natural language,

Reference 15

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source=pdf_text observed=2026-08-02T03:25:50.257058Z digest=sha256:71cf43fe5e76ff283c7ca6d97b2c44fde3a469bf09f02155c9b4a93a2a18e5ca

Observation ad823f21-6f4d-4672-a395-80f344393ab3 · outbound

This paper cites Adapting frechet audio distance for generative music evaluation,.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Adapting frechet audio distance for generative music evaluation,

Reference 16

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source=pdf_text observed=2026-08-02T03:25:50.260384Z digest=sha256:96507413c9cc3ef42ce835649d45982cc9b14897dd361dcbb62e52ec43d8bf1e

Observation c0c24f11-2f11-42f6-8fd6-1455da69ed83 · outbound

This paper cites Yue: Scaling open foundation models for long-form music genera- tion,.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Yue: Scaling open foundation models for long-form music genera- tion,

Reference 17

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source=pdf_text observed=2026-08-02T03:25:50.263726Z digest=sha256:ed3f5140de51f0d3dc44fed1999685781e13a0178bd595df58a75af19d8d1895

Observation 5a9e46de-9faa-4903-bd0e-2cc0267a3d6b · outbound

This paper cites From aesthetics to human preferences: Compara- tive perspectives of evaluating text-to-music systems,.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation From aesthetics to human preferences: Compara- tive perspectives of evaluating text-to-music systems,

Reference 18

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source=pdf_text observed=2026-08-02T03:25:50.267189Z digest=sha256:47ba6cd1a758a0046b7a027c197742e856b08797b49d0a86feab963c063e11ba

Observation 5e5b22d4-da3c-4a2b-9240-2e9286c47b05 · outbound

This paper cites The icassp 2026 automatic song aesthetics evaluation challenge,.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation The icassp 2026 automatic song aesthetics evaluation challenge,

Reference 19

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source=pdf_text observed=2026-08-02T03:25:50.270513Z digest=sha256:2dd4cd56f17fc29a4d7139c69252e6d60dbe2a0509432bf1945249b20e63b6a6

Observation 483f2294-99c3-441c-9895-cc46296fcd3d · outbound

This paper cites Robust learning from noisily labeled long- tailed data via fairness regularizer,.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Robust learning from noisily labeled long- tailed data via fairness regularizer,

Reference 20

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source=pdf_text observed=2026-08-02T03:25:50.274174Z digest=sha256:0e991ba7d618b99fa649d0817ef90bc61528ee5db6c302b179bfee3e9ee5ff5b

Observation ea2f2d44-07aa-414f-98cc-099073cb692b · outbound

This paper cites DiffRhythm+: Controllable and Flexible Full-Length Song Generation with Preference Optimization.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation DiffRhythm+: Controllable and Flexible Full-Length Song Generation with Preference Optimization

Reference 21

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source=pdf_text observed=2026-08-02T03:25:50.277562Z digest=sha256:bc74dc039098a6f854e1bda0fc455c0db24c47ef83014a8f8f95dea0c113d792

Observation db7d261c-a992-4881-837a-d860a877bf17 · outbound

This paper cites ACE-Step: A Step Towards Music Generation Foundation Model.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation ACE-Step: A Step Towards Music Generation Foundation Model

Reference 22

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source=pdf_text observed=2026-08-02T03:25:50.281276Z digest=sha256:c9c68e807c9359f95c121e8d52993b8cf1848b490ec7f59bd2692a26dc73cabb

Observation 8cfdcfd5-9973-4f87-bf99-0136d5f53417 · outbound

This paper cites Levo: High- quality song generation with multi-preference align- ment,.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Levo: High- quality song generation with multi-preference align- ment,

Reference 23

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source=pdf_text observed=2026-08-02T03:25:50.286215Z digest=sha256:7f4d28fcd64ad92f3409d6977381febb1db30cad82d5027a47b9aaf67b5991fc

Observation e521509e-55ca-4174-97c4-7dc62838335b · outbound

This paper cites JAM: A Tiny Flow-based Song Generator with Fine-grained Controllability and Aesthetic Alignment.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation JAM: A Tiny Flow-based Song Generator with Fine-grained Controllability and Aesthetic Alignment

Reference 24

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source=pdf_text observed=2026-08-02T03:25:50.289899Z digest=sha256:dcdc66de08c73763e0cfe965771c88d4833d9a427341dcd8cfe8a13b58db4f3c

Observation 26e5f61f-fc59-46e6-9c75-2370bb031194 · outbound

This paper cites Songecho: Towards cover song generation via instance-adaptive element-wise linear modulation,.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Songecho: Towards cover song generation via instance-adaptive element-wise linear modulation,

Reference 25

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source=pdf_text observed=2026-08-02T03:25:50.293605Z digest=sha256:80b66c97de88b821d654cdc4837edb05cd2b4de70fb96e820705c1eee558e57a

Observation 3d6ae314-cffb-4667-9ee3-5abcfd4a0515 · outbound

This paper cites The mtg-jamendo dataset for automatic mu- sic tagging.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation The mtg-jamendo dataset for automatic mu- sic tagging

Reference 26

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source=pdf_text observed=2026-08-02T03:25:50.296969Z digest=sha256:648e864bc91278d40a506c7254f0f1ae60b0e98dae03e105b68f0b3f3fb71029

Observation 8b920a9f-0e18-4fb4-95bb-3faf0c70d605 · outbound

This paper cites M6: multi- generator, multi-domain, multi-lingual and cultural, multi-genres, multi-instrument machine-generated mu- sic detection databases,.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation M6: multi- generator, multi-domain, multi-lingual and cultural, multi-genres, multi-instrument machine-generated mu- sic detection databases,

Reference 27

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source=pdf_text observed=2026-08-02T03:25:50.300208Z digest=sha256:c35bc65b441a9bd9794fa45a34ef2ca5eced8ac65a95cc985bb99e043aa85eab

Observation a180b172-28f9-464a-b4dc-3cb3e1f281ce · outbound

This paper cites CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction

Reference 28

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Observation 9abf13a8-06f0-42b7-b197-4b29a214b8fb · outbound

This paper cites Qwen3-Omni Technical Report.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Qwen3-Omni Technical Report

Reference 29

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Pith citing papers

Observation 59db3020-5b1a-4ea4-9bc9-f77832a7e587 · inbound

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation cites this paper.

Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation

Reference 1

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