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REVIEW 3 major objections 4 minor 38 references

From Prompting to Describing: A Cross-Cultural Study of Language for AI-Generated Music

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Prompting a music generator and describing the music you hear are structurally different acts of language: genre and story dominate prompts, while instruments, mood, and music theory dominate descriptions.

desk verdict A plausible and useful first pass at a real question, but the LLM labeling validation is too thin to support the strongest claims. read the letter →

arxiv 2608.06634 v1 pith:T5UZ5YBW submitted 2026-08-06 cs.SD

Sangheon Park , Claire Arthur This is my paper · ORCID
classification cs.SD
keywords text-to-musicgenerationpromptlanguagemusicdescriptionsemanticalignmentcross-culturalperceptiontaxonomyofmusicallisteningexperiment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that prompting a text-to-music system and describing heard music are not two ways of saying the same thing but structurally different communicative acts. Using 200 real prompts paired with free-form descriptions from English and Korean listeners, it builds a human-derived taxonomy of seven dimensions of musical language and finds that prompts are dominated by genre and story, while descriptions are richer in instrumentation, mood, and music theory. The paper argues this asymmetry matters because text-to-music models are trained on metadata-style labels rather than on the language listeners actually use, and because narrative-heavy prompts predict the largest mismatch between what a prompt imagines and what listeners hear.

What carries the argument

The load-bearing instrument is a seven-category human-derived taxonomy of musical vocabulary (genre, mood/emotion, instrumentation, music theory, timbre, function, story/narrative), constructed by two annotators from a sample of real prompts. Coding every prompt and description into this taxonomy, with presence and density measures, plus word-level survival and association tests and sentence-embedding cosine similarity, lets the paper compare prompting and describing across category, word, and vector levels. Mixed-effects regressions with random intercepts for text, stimulus, and participant separate category-level differences from item and participant noise.

What would settle it

Have human annotators manually code every prompt and every description using the same seven-category taxonomy, then recompute the presence and density regressions; if human labels do not reproduce the genre/story dominance of prompts and the instrumentation/mood dominance of descriptions, the central asymmetry is an artifact of the automatic labeler.

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Extended reading notes

Core claim

The central discovery is a quantitative linguistic asymmetry: genre appears in about 95% of prompts but only about 61.5% of descriptions, while instrumentation, mood/emotion, and music theory are significantly more common in descriptions than in prompts. Genre terms travel best from prompt to perception, often as semantic neighbors rather than the same word, whereas narrative-heavy prompts show the weakest prompt-to-description semantic similarity, with narrative density nearly three times higher in low-alignment prompts. The paper interprets this not as failed generation but as a structural fact: narrative intent has no acoustic trace, so no amount of generative capacity can recover it. Cross-culturally, Korean and English descriptions differ in emphasis, with Korean listeners allocating more lexical space to mood/emotion, narrative, and function, and English listeners more to genre and music theory.

Load-bearing premise

Everything rests on the automatic taxonomy labeler being accurate enough, even though it was checked against human coding on only 25 English prompts; if that labeler carries language- or category-specific bias, the prompt-description asymmetries and the Korean/English differences could be artifacts of the labeling tool rather than of the language people use.

Editorial extensions

If this is right

  • If prompts and descriptions are structurally different registers, text-to-music evaluation that relies on prompt-style metadata or automatically generated captions may not reflect how listeners actually describe music.
  • Genre labels are the most reliable bridge from prompt to perception, so systems aiming for semantic alignment should favor genre and concrete sonic markers over narrative framing.
  • Narrative-heavy prompts will keep producing low semantic alignment unless text-to-music systems learn to encode the acoustic features that support shared narrative perception.
  • Current English-centric training data may disadvantage users whose natural descriptive vocabulary is affective, narrative, or functional, as the English-Korean comparison suggests.
  • The contributed taxonomy can be reused as an annotation scheme for other prompt corpora and listening studies.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Left implicit in the paper: the same prompt-description asymmetry may hold for other generative modalities such as text-to-image or text-to-video, where narrative prompts are also common; testing the taxonomy in those domains would show whether the genre-versus-narrative split is music-specific.
  • A natural extension is to collect non-English prompts, not just non-English descriptions, to see whether the prompt-description gap itself shrinks or widens when both ends of the chain use the same non-English language.
  • If the cross-cultural pattern is real, text-to-music systems could be evaluated in a language-aware way, comparing model outputs against the distribution of listener descriptions in each language rather than a single aggregated English norm.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This paper studies the linguistic gap between text prompts used to generate music and free-form descriptions of the resulting audio. The authors pair 200 real-world Udio prompts with their generated audio, collect 2,624 free-form descriptions from English-speaking (n=70) and Korean-speaking (n=78) listeners, and propose a seven-category human-derived taxonomy (Genre, Mood/Emotion, Instrumentation, Music Theory, Timbre, Function, Story/Narrative). Using GPT-5.4 to label all texts with these categories, they fit mixed-effects models for category presence and density, perform word-level survival-rate and chi-square association analyses, compute Sentence-BERT prompt-description cosine similarities, and compare high- versus low-alignment prompt groups. The main findings are that prompts are dominated by Genre and Story/Narrative, descriptions are richer in Instrumentation, Mood/Emotion, and Music Theory, genre terms propagate most reliably from prompt to perception, and narrative-heavy prompts are associated with the largest semantic misalignment. A preliminary cross-cultural comparison suggests that Korean descriptions reallocate lexical space toward affective, narrative, and functional framing relative to English descriptions.

Significance. If the findings hold, the paper makes a useful empirical contribution to music information retrieval and human-AI interaction: it moves beyond prompt taxonomies per se and directly measures how prompting language relates to the language of perceptual evaluation, using real-world prompts and paired audio rather than synthetic or curated materials. The human-derived taxonomy, the parallel English/Korean listener populations, the mixed-effects modeling that accounts for text, stimulus, and participant non-independence, and the triangulation of category-level, word-level, and vector-level evidence are genuine strengths. The cross-cultural comparison is explicitly exploratory but opens a question that is largely absent from TTM evaluation. The central claims are, however, only as strong as the automatic taxonomy labeling on which all category-level analyses rest, and that labeling is currently validated on a very small and partially mismatched sample.

major comments (3)
  1. [§4.1, §5.3, §7] The load-bearing measurement assumption is not adequately validated. GPT-5.4 is described as selected on "a held-out, manually-annotated subset 25 of prompts from the Song Describer dataset," but no per-category agreement, confusion matrix, or reliability statistic is reported, and no validation is provided for the actual Udio prompts, for listener descriptions, or for Korean text. Every category-level result in §4.1, §4.2, §4.3, and §4.4 is computed from this labeling, so a systematic labeler bias (e.g., assigning Genre/Story more readily to short noun-phrase prompts and Mood/Instrumentation to longer descriptive sentences, or handling Korean less reliably) could produce the central prompt-description asymmetry and cross-cultural profile differences as artifacts. The paper's own §5.3 admits that classification noise is "difficult to fully quantify." I ask for either a full validation on human-coded material drawn from the actual corpora (including both languages and both text types, with per-category agreement) or a robustness reanalysis of a human-coded subset that directly supports the main interaction and cross-cultural effects.
  2. [§4.3, Table 3] The claim that "narrative-heavy prompts are the strongest predictor of semantic misalignment" is not supported by the analysis as presented. Table 3 compares the top and bottom 25% of prompts by mean prompt-description cosine similarity on one category at a time; it does not jointly test all categories, does not control for prompt length, genre density, or other categories, and does not estimate a predictive model of alignment. The descriptive pattern (Story/Narrative density 0.452 in low-alignment vs. 0.169 in high-alignment prompts) is suggestive, but the word "strongest" requires a joint model, e.g., a regression with all category densities predicting alignment, or at least a model that includes the other categories as covariates. Please either provide such an analysis or soften the claim to "narrative-heavy prompts show the largest univariate gap between high- and low-alignment groups."
  3. [§4.4, §5.3] The cross-cultural comparison is difficult to interpret as a cultural effect because the language groups differ in recruitment channel (single university psychology pool vs. volunteer personal networks plus Prolific), compensation, and number of stimuli assigned (20 vs. 10 for volunteer Korean participants). The paper acknowledges these confounds in §5.3, but the abstract and conclusion state the cross-cultural result as a substantive finding. At minimum, the authors should either report analyses restricted to the comparable Prolific-recruited Korean subgroup versus the English participants, or explicitly characterize the Korean vs. English differences as confounded by participant pool and sampling procedure. Without such a robustness check, the cross-cultural redistribution claim is not yet supported.
minor comments (4)
  1. [§4.2] The chi-square association analysis tests a large number of prompt-description word pairs and reports only positive associations, but no multiple-comparison correction or false-discovery-rate control is described. The specific threshold choices (prompt word occurring more than ten times, at least twenty paired descriptions, co-occurrence at least three times) are reasonable but should be justified or varied in a sensitivity analysis.
  2. [§3.1, Table 1] The taxonomy is derived from prompts only and then applied to descriptions; categories such as Function or Story/Narrative may have different boundaries in perceptual descriptions. The paper should note this construct-validity limitation more explicitly, or provide a small demonstration that the taxonomy covers description language without forcing descriptions into prompt-derived categories.
  3. [§4.2] The concreteness analysis excludes unmatched tokens and reports coverage of 80.3% for prompts and 91.4% for descriptions; the difference in coverage rates could itself affect the prompt-description concreteness comparison. A sensitivity analysis including all tokens or using a different norm set would strengthen the claim.
  4. [§4.1] Equation (1) defines the mixed-effects model, but the description of the random-effects structure for prompts is slightly confusing: prompts have no human author, so the participant random intercept is estimated from descriptions only. This is fine, but the text should state explicitly that prompt observations therefore have a structurally different random-effects design, which may affect variance estimates for the corpus comparison.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the central claims are empirical measurements of prompt versus description language, and the acknowledged LLM-labeling limitation is a measurement-validity concern, not a derived claim reduced to its inputs.

full rationale

No circular steps were identified. The paper's central claims are descriptive measurements: prompts and descriptions are coded with a human-derived taxonomy, and category presence/density, word-association, concreteness, and embedding-similarity statistics are computed from the data. There is no fitted parameter that is subsequently renamed as a prediction, and no equation defines a result in terms of its own outcome. The taxonomy was constructed by human annotators from 100 prompts (Section 3.1), and applying that taxonomy to code prompts and descriptions is a content-analysis procedure, not a derivation of the conclusion from the taxonomy. The GPT-5.4 labeling step (Section 4.1) is a measurement tool; its validation on 25 Song Describer prompts and the absence of per-category agreement or Korean-text validation are validity/generalizability limitations, explicitly acknowledged in Section 5.3: 'our automated LLM-based labeling, though validated against human judgments, introduces classification noise whose downstream effects on the mixed-effects models are difficult to fully quantify.' Such limitations could threaten the robustness of the category-level asymmetries, but they do not make the claimed results equivalent to their inputs by construction. The empirical anchors (Udio audio, listener descriptions, chi-square word associations, Sentence-BERT similarities) are independent of the coding scheme's own definitions. The cross-cultural comparison is explicitly exploratory (Section 4.4), and the citation of Morrison and Yeh [15] is used as related prior evidence rather than as a substitute for the paper's own analysis. The paper is therefore self-contained against its external benchmarks, and the appropriate circularity score is 0.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

This is an empirical study with no mathematical derivation, so the central claims depend on measurement assumptions and hand-chosen analysis thresholds rather than on fitted constants. The mixed-effects coefficients are statistical estimates, not free parameters introduced to make a result true; the two thresholds listed above are the hand-set choices that most directly shape the headline word-level and alignment-group findings.

free parameters (2)
  • High/low alignment quartile cutoff = top and bottom 25% (n=50 each)
    Hand-chosen split of the 200 songs by mean prompt-description cosine similarity. This defines the groups used to conclude that Story/Narrative is the strongest predictor of misalignment.
  • Word-level chi-square frequency thresholds = wp occurred in >10 prompts; wd co-occurred >=3 times; paired with >=20 descriptions
    Hand-chosen inclusion criteria for Table 2. These thresholds restrict the survival analysis to frequent genre and instrument terms, which may partly explain why no mood or narrative terms produced reliable associations.
assumptions (5)
  • domain assumption The seven taxonomy categories are mutually exclusive, comprehensive, and carry the same meaning in prompts and in descriptions.
    Table 1; the taxonomy was built from 100 prompts and then applied to all texts. If descriptions contain dimensions outside these categories, the category-level comparisons miss them.
  • domain assumption GPT-5.4 taxonomy labels, validated on 25 English prompts from Song Describer, are reliable for all prompts and all English and Korean descriptions.
    Invoked in Section 4.1 and the AI Usage Statement; the validation sample is tiny, drawn from a different corpus, and no Korean-language validation is reported.
  • domain assumption The initial 32-second Udio clip is a direct acoustic realization of the user's prompt, unobscured by extensions or re-prompting.
    Section 3.2.2; if Udio's generation pipeline rewrites or reconditions prompts, the prompt-perception comparisons reflect system behavior rather than language alone.
  • domain assumption Sentence-BERT cosine similarity between a prompt and a description captures semantic alignment.
    Section 4.3; no evidence is given that this embedding space aligns generative intent with perceptual language, though it is a standard tool.
  • standard math The mixed-effects models with random intercepts for text, stimulus, and participant correctly handle non-independence.
    Equation (1) uses standard lme4 assumptions; this is a normal statistical modeling choice rather than a domain-specific assumption.

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Cite this review

Pith. "Pith review of From Prompting to Describing: A Cross-Cultural Study of Language for AI-Generated Music." pith.science (2026). https://pith.science/paper/T5UZ5YBW

@misc{pith2026260806634,
  author       = {Pith},
  title        = {Pith review of: From Prompting to Describing: A Cross-Cultural Study of Language for AI-Generated Music},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T5UZ5YBW}},
  note         = {Machine review of arXiv:2608.06634}
}
read the original abstract

Text-to-music (TTM) generation systems allow users to create music through natural language prompts, yet it is unclear whether the descriptive language used to prompt aligns with descriptive language used to summarize or describe heard music. We pair 200 real-world Udio prompts with their generated audio and free-form descriptions collected from English- (n = 70) and Korean-speaking (n = 78) listeners, and contribute a human-derived taxonomy of musical prompting vocabulary grounded in real user data. Using this framework, alongside word- and vector-level analyses, we find a consistent structural asymmetry: prompts are dominated by Genre and Story/Narrative language. Genre terms propagate most reliably from prompt to perception, while narrative-heavy prompts are the strongest predictor of semantic misalignment. A preliminary cross-cultural comparison further suggests that description profiles vary across listener populations along narrative, functional, and affective dimensions, raising questions about whether current TTM systems, trained on aggregated English-centric corpora, can accommodate the full diversity of how people naturally express musical ideas.

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Reference graph

Works this paper leans on

38 extracted references · 33 canonical work pages

  1. [1]

    From Prompting to Describing: A Cross-Cultural Study of Language for AI-Generated Music

    INTRODUCTION Recent text-to-music (TTM) systems such as Suno [2], Udio [1], and Google’s MusicFX [3] allow users to gen- erate music from a natural language prompt, lowering the barrier to music creation for users without specialized mu- sical knowledge [4]. Understanding how users write these prompts is therefore a fundamental question for music informat...

    work page 2026

  2. [2]

    From Prompting to Describing: A Cross-Cultural Study of Language for AI-Generated Music

    BACKGROUND Research on prompt construction has emerged primarily in the text-to-image domain. Oppenlaender [9] identified six types of promptmodifiersthrough ethnographic study, while subsequent work documented increasing lexical ho- mogenization as user communities converge on effective patterns [10]. Prompt analysis in text-to-music remains comparativel...

    work page Pith review arXiv 2026

  3. [3]

    While Casini et al

    METHODS 3.1 Taxonomy Construction In order to compare descriptions, it was important to have a rubric or taxonomy with which to base the comparison. While Casini et al. [8] did build a taxonomy, theirs was en- tirely generated automatically by a model. Given that we wished to ground our comparison in human perception, we sought to create a similar taxonom...

    work page 2026

  4. [4]

    Because manual coding at scale is highly laborious, we employed GPT-5.4

    RESULTS 4.1 Taxonomic Analysis To compare prompts and descriptions, we first coded all texts according to our taxonomy. Because manual coding at scale is highly laborious, we employed GPT-5.4. The GPT-5.4 model was selected following a pilot evaluation in which multiple LLM configurations were compared on a held-out, manually-annotated subset 25 of prompt...

    work page

  5. [5]

    DISCUSSION 5.1 The Prompt-Description Gap Prompts and descriptions are not two registers of the same language but structurally different communica- tive acts. Prompts are dominated byGenreand Story/Narrative/Lyrics(95% and 74% presence respec- Figure 2: Cross-cultural description comparison by language group (purple: English,n= 70; green: Korean,n= 78): (...

    work page

  6. [6]

    Prompts and descriptions emerge as structurally distinct registers:GenreandStory/Narrativedominate prompts, whileInstrumentation,Mood/Emotion, andMusic Theory dominate descriptions

    CONCLUSION We characterized the linguistic gap between prompting text-to-music systems and describing the resulting audio. Prompts and descriptions emerge as structurally distinct registers:GenreandStory/Narrativedominate prompts, whileInstrumentation,Mood/Emotion, andMusic Theory dominate descriptions. Genre vocabulary propagates most reliably from promp...

    work page

  7. [7]

    AI USAGE STA TEMENT Large language models were used as a component of the study’s methodology. As described in Section 4, GPT-5.4 was used to assign taxonomic category labels to all prompt and description texts; model selection, prompting proce- dure, and validation against human-coded data are reported there and in the supplementary material. The taxonom...

    work page

  8. [8]

    Data-driven analysis of text-conditioning in ai-generated music: A case study with suno and udio,

    L. Casini, L. C. Vila, D. Dalmazzo, A.-K. Kaila, and B. L. Sturm, “Data-driven analysis of text-conditioning in ai-generated music: A case study with suno and udio,”Transactions of the International Society for Music Information Retrieval, May 2026

    work page 2026

Show all 38 references
  1. [9]

    Udio ai music generation platform,

    Udio AI, “Udio ai music generation platform,” https: //www.udio.com, 2026, accessed: 2026-04-18; Version 4.0 Allegro

    2026

  2. [10]

    Suno ai music generation model,

    Suno AI, “Suno ai music generation model,” https:// www.suno.com, 2026, accessed: 2026-04-18; Version 5.5

    2026

  3. [11]

    Musicfx: Generative ai music ex- periment,

    Google DeepMind, “Musicfx: Generative ai music ex- periment,” https://aitestkitchen.withgoogle.com/tools/ music-fx, 2026, accessed: 2026-04-18; Powered by Lyria RealTime model; DJ Mode

    2026

  4. [12]

    Ai-enabled text-to-music generation: A comprehensive review of methods, frameworks, and future directions,

    Y . Zhao, M. Yang, Y . Lin, X. Zhang, F. Shi, Z. Wang, J. Ding, and H. Ning, “Ai-enabled text-to-music generation: A comprehensive review of methods, frameworks, and future directions,” Electronics, vol. 14, no. 6, 2025. [Online]. Available: https://www.mdpi.com/2079-9292/14/6/1197

    2025

  5. [13]

    Is writing prompts really making art?

    J. McCormack, C. C. Gambardella, N. Rajcic, S. J. Krol, M. T. Llano, and M. Yang, “Is writing prompts really making art?” 2023. [Online]. Available: https://arxiv.org/abs/2301.13049

    2023 arXiv

  6. [14]

    Simple and controllable music generation,

    J. Copet, F. Kreuk, I. Gat, T. Remez, D. Kant, G. Synnaeve, Y . Adi, and A. Défossez, “Simple and controllable music generation,” 2024. [Online]. Available: https://arxiv.org/abs/2306.05284

    2024 arXiv

  7. [15]

    Music for all: Rep- resentational bias and cross-cultural adaptability of music generation models,

    A. Mehta, S. Chauhan, A. Djanibekov, A. Kulkarni, G. Xia, and M. Choudhury, “Music for all: Rep- resentational bias and cross-cultural adaptability of music generation models,” 2025. [Online]. Available: https://arxiv.org/abs/2502.07328

    2025 arXiv

  8. [16]

    Cultural constraints on music perception and cognition,

    S. J. Morrison and S. M. Demorest, “Cultural constraints on music perception and cognition,” in Cultural Neuroscience: Cultural Influences on Brain Function, ser. Progress in Brain Research, J. Y . Chiao, Ed. Elsevier, 2009, vol. 178, pp. 67–77. [Online]. Available: https://ww...

    2009

  9. [17]

    A taxonomy of prompt mod- ifiers for text-to-image generation,

    J. Oppenlaender, “A taxonomy of prompt mod- ifiers for text-to-image generation,”Behaviour & Information Technology, vol. 43, no. 15, pp. 3763–3776, 2024. [Online]. Available: https: //doi.org/10.1080/0144929X.2023.2286532

    2024

  10. [18]

    A prompt log analysis of text-to-image generation systems,

    Y . Xie, Z. Pan, J. Ma, L. Jie, and Q. Mei, “A prompt log analysis of text-to-image generation systems,” in Proceedings of the ACM Web Conference 2023, ser. WWW ’23. New York, NY , USA: Association for Computing Machinery, 2023, p. 3892–3902. [Online]. Available: https://doi.o...

    2023

  11. [19]

    The interpretation gap in text-to-music generation models,

    Y . Zang and Y . Zhang, “The interpretation gap in text-to-music generation models,” 2024. [Online]. Available: https://arxiv.org/abs/2407.10328

    2024 arXiv

  12. [20]

    Paguri: A user experience study of creative interaction with text-to-music models,

    F. Ronchini, L. Comanducci, G. Perego, and F. An- tonacci, “Paguri: A user experience study of creative interaction with text-to-music models,”Electron- ics, vol. 14, no. 17, 2025. [Online]. Available: https://www.mdpi.com/2079-9292/14/17/3379

    2025

  13. [21]

    Understanding the potentials and limitations of prompt-based music generative ai,

    Y . Choi, J. Moon, J. Yoo, and J.-H. Hong, “Understanding the potentials and limitations of prompt-based music generative ai,” inProceedings of the 2025 CHI Conference on Human Factors in Computing Systems, ser. CHI ’25. New York, NY , USA: Association for Computing Machinery,...

    2025 doi

  14. [22]

    Iteratta: An interface for exploring both text prompts and audio priors in generating music with text-to-audio models,

    H. Yakura and M. Goto, “Iteratta: An interface for exploring both text prompts and audio priors in generating music with text-to-audio models,” 2023. [Online]. Available: https://arxiv.org/abs/2307.13005

    2023 arXiv

  15. [23]

    Preference responses and use of written descriptors among music and nonmusic majors in the united states, hong kong, and the people’s republic of china,

    S. J. Morrison and C. S. Yeh, “Preference responses and use of written descriptors among music and nonmusic majors in the united states, hong kong, and the people’s republic of china,”Journal of Research in Music Education, vol. 47, no. 1, pp. 5–17,

  16. [24]

    Sentence-bert: Sentence embeddings using siamese bert-networks,

    N. Reimers and I. Gurevych, “Sentence-bert: Sentence embeddings using siamese bert-networks,” inProceed- ings of the 2019 conference on empirical methods in natural language processing and the 9th interna- tional joint conference on natural language processing (EMNLP-IJCNLP), ...

    2019

  17. [25]

    Narratives imagined in response to instrumental music reveal culture-bounded intersubjectivity,

    E. H. Margulis, P. C. M. Wong, C. Turnbull, B. M. Kubit, and J. D. McAuley, “Narratives imagined in response to instrumental music reveal culture-bounded intersubjectivity,”Proceedings of the National Academy of Sciences, vol. 119, no. 4, p. e2110406119, 2022. [Online]. Availa...

    2022 doi

  18. [26]

    The song describer dataset: a corpus of au- dio captions for music-and-language evaluation,

    I. Manco, B. Weck, S. Doh, M. Won, Y . Zhang, D. Bog- danov, Y . Wu, K. Chen, P. Tovstogan, E. Benetos et al., “The song describer dataset: a corpus of au- dio captions for music-and-language evaluation,”arXiv preprint arXiv:2311.10057, 2023

    2023 arXiv

  19. [27]

    Validating the use of large language models for psychological text classification,

    H. L. Bunt, A. Goddard, T. W. Reader, and A. Gille- spie, “Validating the use of large language models for psychological text classification,”Frontiers in Social Psychology, vol. 3, p. 1460277, 2025

    2025

  20. [28]

    K. A. Neuendorf,The content analysis guidebook. sage, 2017

    2017

  21. [29]

    The development and psychometric properties of liwc2015,

    J. W. Pennebaker, R. L. Boyd, K. Jordan, and K. Black- burn, “The development and psychometric properties of liwc2015,” 2015

    2015

  22. [30]

    Fitting linear mixed-effects models using lme4,

    D. Bates, M. Mächler, B. Bolker, and S. Walker, “Fitting linear mixed-effects models using lme4,” Journal of Statistical Software, vol. 67, no. 1, p. 1–48,

  23. [32]

    A user- oriented approach to music information retrieval,

    M. Lesaffre, M. Leman, and j.-p. Martens, “A user- oriented approach to music information retrieval,” 01 2006

    2006

  24. [33]

    Con- creteness ratings for 40 thousand generally known english word lemmas,

    M. Brysbaert, A. B. Warriner, and V . Kuperman, “Con- creteness ratings for 40 thousand generally known english word lemmas,”Behavior research methods, vol. 46, no. 3, pp. 904–911, 2014

    2014

  25. [36]

    Do you hear what i hear? perceived narrative constitutes a semantic dimension for music,

    J. D. McAuley, P. C. Wong, A. Mamidipaka, N. Phillips, and E. H. Margulis, “Do you hear what i hear? perceived narrative constitutes a semantic dimension for music,”Cognition, vol. 212, p. 104712,

  26. [38]

    The musicality of non-musicians: An index for assessing musical sophistication in the general population,

    D. Müllensiefen, B. Gingras, J. Musil, and L. Stewart, “The musicality of non-musicians: An index for assessing musical sophistication in the general population,”PLOS ONE, vol. 9, no. 2, pp. 1–23, 02 2014. [Online]. Available: https://doi.org/10.1371/journal.pone.0089642

    2014 doi

  27. [83]

    were recruited in two waves, an uncompensated volun- teer wave (n= 33) and a Prolific wave (n= 50). Partici- pants who submitted no descriptions were excluded (one English, five Korean), leaving 148 participants (English n= 70; Koreann= 78) and2,624descriptions (English = 1,39...

    2019

  28. [1999]

    Available: http://www.jstor.org/stable/ 3345824

    [Online]. Available: http://www.jstor.org/stable/ 3345824

  29. [2015]

    Available: https://www.jstatsoft.org/ index.php/jss/article/view/v067i01

    [Online]. Available: https://www.jstatsoft.org/ index.php/jss/article/view/v067i01

  30. [2021]

    Available: https://www.sciencedirect

    [Online]. Available: https://www.sciencedirect. com/science/article/pii/S0010027721001311

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