Supporting Creative Ownership through Deep Learning-Based Music Variation

September 30, 2025 Β· Declared Dead Β· πŸ› arXiv.org

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Authors Stephen James Krol, Maria Teresa Llano, Jon McCormack arXiv ID 2509.25834 Category cs.HC: Human-Computer Interaction Cross-listed cs.AI Citations 0 Venue arXiv.org Last Checked 4 months ago
Abstract
This paper investigates the importance of personal ownership in musical AI design, examining how practising musicians can maintain creative control over the compositional process. Through a four-week ecological evaluation, we examined how a music variation tool, reliant on the skill of musicians, functioned within a composition setting. Our findings demonstrate that the dependence of the tool on the musician's ability, to provide a strong initial musical input and to turn moments into complete musical ideas, promoted ownership of both the process and artefact. Qualitative interviews further revealed the importance of this personal ownership, highlighting tensions between technological capability and artistic identity. These findings provide insight into how musical AI can support rather than replace human creativity, highlighting the importance of designing tools that preserve the humanness of musical expression.
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