Supporting Creative Ownership through Deep Learning-Based Music Variation

• URL: http://arxiv.org/abs/2509.25834v2
• Date: Tue, 07 Oct 2025 05:31:15 GMT
• Title: Supporting Creative Ownership through Deep Learning-Based Music Variation
• Authors: Stephen James Krol, Maria Teresa Llano, Jon McCormack,
• Abstract summary: This paper investigates the importance of personal ownership in musical AI design.<n>We examine how practising musicians can maintain creative control over the compositional process.<n>Findings highlight the importance of designing tools that preserve the humanness of musical expression.
• Score: 4.71547360356314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Decoding Selective Auditory Attention to Musical Elements in Ecologically Valid Music Listening [2.9306210138702142]
  This study is the first attempt to decode selective attention to musical elements using naturalistic, studio-produced songs and a lightweight consumer-grade EEG device with only four electrodes.<n>Our contributions are fourfold: (i) decoding music attention in real studio-produced songs, (ii) demonstrating feasibility with a four-channel consumer EEG, (iii) providing insights for music attention decoding, and (iv) demonstrating improved model ability over prior work.
  arXiv  Detail & Related papers  (2025-12-05T08:39:36Z)
• The Ghost in the Keys: A Disklavier Demo for Human-AI Musical Co-Creativity [59.78509280246215]
  Aria-Duet is an interactive system facilitating a real-time musical duet between a human pianist and Aria, a state-of-the-art generative model.<n>We analyze the system's output from a musicological perspective, finding the model can maintain stylistic semantics and develop coherent phrasal ideas.
  arXiv  Detail & Related papers  (2025-11-03T15:26:01Z)
• Exploring the Needs of Practising Musicians in Co-Creative AI Through Co-Design [0.27309692684728604]
  We present a case study that explores the needs of practising musicians through the co-design of a musical variation system.<n>This was achieved through two workshops and a two week ecological evaluation, where musicians from different musical backgrounds offered valuable insights.
  arXiv  Detail & Related papers  (2025-02-13T08:10:07Z)
• A Survey of Foundation Models for Music Understanding [60.83532699497597]
  This work is one of the early reviews of the intersection of AI techniques and music understanding. We investigated, analyzed, and tested recent large-scale music foundation models in respect of their music comprehension abilities.
  arXiv  Detail & Related papers  (2024-09-15T03:34:14Z)
• Foundation Models for Music: A Survey [77.77088584651268]
  Foundations models (FMs) have profoundly impacted diverse sectors, including music. This comprehensive review examines state-of-the-art (SOTA) pre-trained models and foundation models in music.
  arXiv  Detail & Related papers  (2024-08-26T15:13:14Z)
• ComposerX: Multi-Agent Symbolic Music Composition with LLMs [51.68908082829048]
  Music composition is a complex task that requires abilities to understand and generate information with long dependency and harmony constraints. Current LLMs easily fail in this task, generating ill-written music even when equipped with modern techniques like In-Context-Learning and Chain-of-Thoughts. We propose ComposerX, an agent-based symbolic music generation framework.
  arXiv  Detail & Related papers  (2024-04-28T06:17:42Z)
• Interactive Melody Generation System for Enhancing the Creativity of Musicians [0.0]
  This study proposes a system designed to enumerate the process of collaborative composition among humans. By integrating multiple Recurrent Neural Network (RNN) models, the system provides an experience akin to collaborating with several composers.
  arXiv  Detail & Related papers  (2024-03-06T01:33:48Z)
• Redefining Relationships in Music [55.478320310047785]
  We argue that AI tools will fundamentally reshape our music culture. People working in this space could decrease the possible negative impacts on the practice, consumption and meaning of music.
  arXiv  Detail & Related papers  (2022-12-13T19:44:32Z)
• Affective Idiosyncratic Responses to Music [63.969810774018775]
  We develop methods to measure affective responses to music from over 403M listener comments on a Chinese social music platform. We test for musical, lyrical, contextual, demographic, and mental health effects that drive listener affective responses.
  arXiv  Detail & Related papers  (2022-10-17T19:57:46Z)
• Generating Music and Generative Art from Brain activity [0.0]
  This research work introduces a computational system for creating generative art using a Brain-Computer Interface (BCI) The generated artwork uses brain signals and concepts of geometry, color and spatial location to give complexity to the autonomous construction.
  arXiv  Detail & Related papers  (2021-08-09T19:33:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.