Paper year
2025
Paper dossier
PAGURI: A User Experience Study of Creative Interaction with Text-to-Music Models
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Citations
4
Authors
0
Topic labels
0
Source readout
Source and corpus status
Venue
Unknown venue
Source slug
unknown
Corpus placement
Controlled edge slice
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Not available yet
Ranking readout
Where this paper lands in the current run
Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced
This block uses the same resolved ranking run as Recommended. Ranks here are materialized paper_scores ranks; live Emerging may be reordered by the bounded ML scorer. Family rank is global within each family, but rank is only shown when this paper lands inside the surfaced top 50.
Families present
2
Top 50
0
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
Present in run, outside top 50
Emerging: embedding slice fit vs included-corpus centroid (title+abstract), plus citation velocity and topic growth; not universal relevance. Bridge signal not used here.
Signals: semantic=0.8694, citation_velocity=0.2400, topic_growth=0.0000, diversity_penalty=0.0000
Why this surfaced | 3 used | 1 penalty | 1 not computed
Embedding slice fit (corpus centroid)used
Embedding slice fit (corpus centroid): high; used in final ranking (contribution to score: 0.1739)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.1200)
Topic momentumused
Topic momentum: low; used in final ranking (contribution to score: 0.0000)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Similarity penaltypenalty
Similarity penalty: reduces score when non-zero (contribution to score: 0.0000)
Bridge
Present in run, outside top 50
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.2400, topic_growth=0.0000, diversity_penalty=1.0000
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0840)
Topic momentumused
Topic momentum: low; used in final ranking (contribution to score: 0.0000)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Topic breadth penaltypenalty
Topic breadth penalty: reduces score when non-zero (contribution to score: -0.2000)
Under-cited
No materialized row for this family in the resolved run
This paper did not surface into the current materialized family row set.
Abstract
In recent years, text-to-music models have been the biggest breakthrough in automatic music generation. While they are unquestionably a showcase of technological progress, it is not clear yet how they can be realistically integrated into the artistic practice of musicians and music practitioners. This paper aims to address this question via Prompt Audio Generation User Research Investigation (PAGURI), a user experience study where we leverage recent text-to-music developments to study how musicians and practitioners interact with these systems, evaluating their satisfaction levels. We developed an online tool through which users can generate music samples and/or apply recently proposed personalization techniques based on fine-tuning to allow the text-to-music model to generate sounds closer to their needs and preferences. Using semi-structured interviews, we analyzed different aspects related to how participants interacted with the proposed tool to understand the current effectiveness and limitations of text-to-music models in enhancing users' creativity. Our research centers on user experiences to uncover insights that can guide the future development of TTM models and their role in AI-driven music creation. Additionally, they offered insightful perspectives on potential system improvements and their integration into their music practices. The results obtained through the study reveal the pros and cons of the use of TTMs for creative endeavors. Participants recognized the system's creative potential and appreciated the usefulness of its personalization features. However, they also identified several challenges that must be addressed before TTMs are ready for real-world music creation, particularly issues of prompt ambiguity, limited controllability, and integration into existing workflows.
Authors
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