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Paper Citation Record · LEDGER

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

As of 10 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 2026-08-10T06:31:04.303077+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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source=pdf_text observed=2026-08-02T03:25:50.204971Z digest=sha256:5ee9673473890a5929778d430899dae0c14b200de38a9c600b3101b1e7322c01

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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source=pdf_text observed=2026-08-02T03:25:50.213410Z digest=sha256:764651b9c5bdd9d5c1bde97d6c8cd9f6ea6dacc8283bdd1e1fd958a27a8ebb85

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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source=pdf_text observed=2026-08-02T03:25:50.217486Z digest=sha256:add896df39d87cfbf850fd6e151367a63957489de19c3c03bec17f403451803c

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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source=pdf_text observed=2026-08-02T03:25:50.225076Z digest=sha256:0a261cc2e8f2b0664bd194f51a88977b0468a189882de49fdf402a44ef744c3c

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=sha256:4875e69815ee24542de51285f82d19fb0c86523e4e67f8a617d378804e8c409f

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=sha256:0836a7584536f1a13fd53366beffd8f494f3bf36ebb7c3a8d948099f6b956cd0

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=sha256:03799e065d1887337799e770ececdd3b06d0d37bbc10bc6b38ec747bca6a8be7

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:fe5f8b76215883459250c1256658f672c67e21ad8d34feec99bd3a7a339398dc

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=sha256:646e1b3ba5281ef919646abdb8b01ac79d2504c41821331cf2dc02101f321533

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=sha256:01557d562ab60e7359906fba846bfb89b6aa00913344057b58831e28c9984a40

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=sha256:4ceb7e59efbfe49abcb6e67168e2e358a2c1a64bc120c8925254ea5275c32c37

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:c9c998a23122984379e5f5a29e1a77ed8863c5cfba8aba62334485b605fed723

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=sha256:9c7e4303a760b887cad9e35a1ef10f0da816e36365e2c4d865e001464e99141f

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=sha256:5c09d9117b4fc3e1b80fcf02af164d10d0c4f2827cc4a9f1e86e426b3db1029d

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=sha256:58b2ac8be90afb11073f93c31e24fede04e813052a33cad4800c01874fb2c6f6

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=sha256:2bb30de6232d99ce81862b975011403b84e4d4070bbfe2f978f8b0a61b6e282c

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=sha256:931eca3fbc0edb7a9bf159c5142001934954524ca2be42675714462a1dfb839f

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:c746b2bad46714695c4b8c727bddeaf037a6c3530fa357b9bdafea9748c6f209

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=sha256:742f7bff2e4c83c2e0280fb764797288aa6b6b9604efa59fd39b70b373cb8626

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:ab4ddb9bf2479968b08d00db0ac4f4a41e6b97d809e33ef601c7a2ec8bc46942

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:d48bd407a29030f8160bd38d3405780f870b6ba8dd377da82ca0ad996c7590f2

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=sha256:1381e6d9afa16cc7fc2f96c706c4a7f9b7a506595a03607ae7ce888d1c6d6a01

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:ce24877516826a86465958a7d0549c16481dbf572c765021339088117f2ea096

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=sha256:8447b555596d918a4cae862f90b36947f9a7697b2c193b4d1fd338d68034bb7d

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=sha256:5d9bbe27fbce27fd99a4b1361f460b38ac82f07d1921d7833ac7f303eb7d0266

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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