Supporting Creative Ownership through Deep Learning-Based Music Variation
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915535510306816 |
|---|---|
| author | Krol, Stephen James Llano, Maria Teresa McCormack, Jon |
| author_facet | Krol, Stephen James Llano, Maria Teresa McCormack, Jon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25834 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Supporting Creative Ownership through Deep Learning-Based Music Variation Krol, Stephen James Llano, Maria Teresa McCormack, Jon Human-Computer Interaction Artificial Intelligence 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. |
| title | Supporting Creative Ownership through Deep Learning-Based Music Variation |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2509.25834 |