Paper Citation Record · LEDGER

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems

As of 19 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2504.21815.

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

pith.paper-citation-record.v1
2504.21815 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:57:47.433651Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:57:47.249767Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-16T04:57:47.830028Z

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation 4cf1924a-2025-401e-a7b6-1796ad82b4e8 · outbound

This paper cites an unresolved cited work.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Unresolved cited work

Reference 1

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This paper cites From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems

Reference 2

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From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Unresolved cited work

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This paper cites low quality.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems low quality

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This paper cites We examine reference-based evaluation metrics on the generated dataset computed in Section 4, offering a complementary perspective to subjective assessments.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems We examine reference-based evaluation metrics on the generated dataset computed in Section 4, offering a complementary perspective to subjective assessments

Reference 5

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Observation 748cb1b8-505f-46c1-8363-626873488520 · outbound

This paper cites Our results reveal substantial incon- sistencies between different evaluation perspectives, highlighting the challenges of fully capturing human judgment through automated proxies.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Our results reveal substantial incon- sistencies between different evaluation perspectives, highlighting the challenges of fully capturing human judgment through automated proxies

Reference 6

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This paper cites Acoustic scene generation with condi- tional SampleRNN,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Acoustic scene generation with condi- tional SampleRNN,

Reference 7

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Observation 1ec9efc9-90c6-45da-99da-6d7fb5062a42 · outbound

This paper cites AudioGen: Textually guided audio genera- tion,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems AudioGen: Textually guided audio genera- tion,

Reference 8

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Observation 263790c9-df72-4cc2-b01e-e74fa0af038f · outbound

This paper cites DExter: Learning and Controlling Performance Expression with Diffusion Models,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems DExter: Learning and Controlling Performance Expression with Diffusion Models,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:48.118563Z

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Observation e15067ec-328f-4ba1-a97f-963ca8c703f6 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 10

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Observation dea05df3-4aa9-463e-90b1-868cb15c4b7f · outbound

This paper cites Qwen2.5 Technical Report.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Qwen2.5 Technical Report

Reference 11

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Observation 21984b2c-a8a6-4ea1-b1a0-66d8afc92516 · outbound

This paper cites DSPO: Direct score preference optimization for diffusion model align- ment,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems DSPO: Direct score preference optimization for diffusion model align- ment,

Reference 12

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Observation 0a4454cb-d7b0-496d-835e-1e617e964cd5 · outbound

This paper cites Seed-TTS: A Family of High-Quality Versatile Speech Generation Models.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Seed-TTS: A Family of High-Quality Versatile Speech Generation Models

Reference 13

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This paper cites 1, Association for Computing Machinery, 2024.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems 1, Association for Computing Machinery, 2024

Reference 14

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Observation 04fec364-3565-464e-8d24-9d25be516a88 · outbound

This paper cites BATON: Aligning text-to-audio model using human preference feedback,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems BATON: Aligning text-to-audio model using human preference feedback,

Reference 15

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Observation 270de9c1-f454-41b2-9ba2-b54894bab1ae · outbound

This paper cites DRAGON: Distributional rewards optimize diffusion generative models,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems DRAGON: Distributional rewards optimize diffusion generative models,

Reference 16

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Observation bf4d53e4-f123-46d0-83fa-e685f3268f87 · outbound

This paper cites SMART: Tuning a symbolic music generation system with an audio domain aesthetic reward.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems SMART: Tuning a symbolic music generation system with an audio domain aesthetic reward

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:57:47.709388Z

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Observation 9858a7fc-3339-4148-9bae-3beae96a5fad · outbound

This paper cites Aligning Text-to-Music Evaluation with Human Preferences.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Aligning Text-to-Music Evaluation with Human Preferences

Reference 18

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Observation a78cb8af-1d39-436a-8cd2-25fcb146a866 · outbound

This paper cites KAD: No More FAD! An Effective and Efficient Evaluation Metric for Audio Generation.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems KAD: No More FAD! An Effective and Efficient Evaluation Metric for Audio Generation

Reference 19

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This paper cites WavCraft: Audio editing and generation with large language models,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems WavCraft: Audio editing and generation with large language models,

Reference 20

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Observation 6a11a3b2-b24f-4853-87a4-2747096b6ca4 · outbound

This paper cites WavJourney: Compositional audio creation with large language models,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems WavJourney: Compositional audio creation with large language models,

Reference 21

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Observation 1216f0f5-e14e-4400-9816-8a87becf1229 · outbound

This paper cites Bridging Paintings and Music -- Exploring Emotion based Music Generation through Paintings.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Bridging Paintings and Music -- Exploring Emotion based Music Generation through Paintings

Reference 22

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Observation fe47a6b5-73ee-402c-b1a6-0c9ebede57bb · outbound

This paper cites Hierarchical Symbolic Pop Music Generation with Graph Neural Networks.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Hierarchical Symbolic Pop Music Generation with Graph Neural Networks

Reference 23

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This paper cites RenderBox: Expressive Performance Rendering with Text Control.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems RenderBox: Expressive Performance Rendering with Text Control

Reference 24

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This paper cites Leveraging pre-trained audioldm for sound generation: A benchmark study,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Leveraging pre-trained audioldm for sound generation: A benchmark study,

Reference 25

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This paper cites Diffsound: Discrete diffu- sion model for text-to-sound generation,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Diffsound: Discrete diffu- sion model for text-to-sound generation,

Reference 26

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Observation 6b059db3-0c0e-465d-9a3b-635fc6f088b5 · outbound

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

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Adapting frechet audio distance for generative music evaluation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:48.007803Z

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Observation 55d0523a-c8ad-4a58-8426-2b6794426a8a · outbound

This paper cites Zero-shot unsupervised and text-based audio editing using DDPM inversion,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Zero-shot unsupervised and text-based audio editing using DDPM inversion,

Reference 28

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Observation 09b81ee9-7b6f-4e12-9723-5b6787abab2b · outbound

This paper cites AudioMorphix: Training-free audio editing with diffu- sion probabilistic models,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems AudioMorphix: Training-free audio editing with diffu- sion probabilistic models,

Reference 29

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Observation ad3c2866-b980-487e-ab89-467c7df63f34 · outbound

This paper cites A comparison of deep learning MOS pre- dictors for speech synthesis quality,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems A comparison of deep learning MOS pre- dictors for speech synthesis quality,

Reference 30

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Observation 1558adc6-5a1b-4109-83b4-9dde11ff723e · outbound

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

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound

Reference 31

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This paper cites From Audio Encoders to Piano Judges: Benchmarking Performance Under- standing for Solo Piano,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems From Audio Encoders to Piano Judges: Benchmarking Performance Under- standing for Solo Piano,

Reference 32

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From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Piano Skills Assessment,

Reference 33

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Observation 998adf1f-4074-453f-bba7-18b6777c6225 · outbound

This paper cites LLaQo: Towards a Query-Based Coach in Expressive Music Performance Assessment,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems LLaQo: Towards a Query-Based Coach in Expressive Music Performance Assessment,

Reference 34

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This paper cites MusicEval: A Generative Music Dataset with Expert Ratings for Automatic Text-to-Music Evaluation.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems MusicEval: A Generative Music Dataset with Expert Ratings for Automatic Text-to-Music Evaluation

Reference 35

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Observation c6744b11-d13b-4c25-896e-f467dc0231f9 · outbound

This paper cites How does the teacher rate? Observations from the NeuroPiano dataset,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems How does the teacher rate? Observations from the NeuroPiano dataset,

Reference 36

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Observation 3f4fcfd6-91c2-40c6-9266-2dc96d8a1da0 · outbound

This paper cites Joint Audio and Symbolic Conditioning for Temporally Controlled Text-to-Music Generation,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Joint Audio and Symbolic Conditioning for Temporally Controlled Text-to-Music Generation,

Reference 37

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This paper cites Stable Audio Open.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Stable Audio Open

Reference 38

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This paper cites Simple and controllable music generation,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Simple and controllable music generation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:47.878743Z

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

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems Yue: Scaling open foundation models for long-form music generation,

Reference 40

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This paper cites DiffRhythm: Blazingly Fast and Embarrassingly Simple End-to-End Full-Length Song Generation with Latent Diffusion.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems DiffRhythm: Blazingly Fast and Embarrassingly Simple End-to-End Full-Length Song Generation with Latent Diffusion

Reference 41

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This paper cites LP-MusicCaps: LLM-based pseudo music captioning,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems LP-MusicCaps: LLM-based pseudo music captioning,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:47.864251Z

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Observation b11a7715-a5a8-4d16-a5a2-6f7b40aeebd0 · outbound

This paper cites PANNs: Large-scale pre- trained audio neural networks for audio pattern recognition,.

From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems PANNs: Large-scale pre- trained audio neural networks for audio pattern recognition,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:57:47.849442Z

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Pith citing papers

Observation 6d90708e-c42e-408a-b0ac-d99b91e74eb1 · inbound