When Strings Tug at Algorithm: Human-AI Sovereignty and Entanglement in Nomadic Improvisational Music Performance as a Decolonial Exploration

• URL: http://arxiv.org/abs/2510.19086v1
• Date: Tue, 21 Oct 2025 21:24:17 GMT
• Title: When Strings Tug at Algorithm: Human-AI Sovereignty and Entanglement in Nomadic Improvisational Music Performance as a Decolonial Exploration
• Abstract summary: This research interrogates power dynamics in human-AI sovereignty and entanglement for nomadic improvisational Dutar performance.<n>The author concludes that decolonial potential requires redesigning tools or systems for cultural survivance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As emergent artificial intelligence technologies increasingly assert roles as assistants within intangible cultural heritage contexts, researchers and artists observe existing questions on the theme of agency negotiation, cultural resistance, and technical critique. This research interrogates power dynamics in human-AI sovereignty and entanglement for nomadic improvisational Dutar performance, a living cultural heritage through a long-necked lute from the Central Asia region. To investigate tensions between human agency and computational hegemony, the researcher and artists examined and iterated a feedback workflow that captures live performance data, processes digital transformations, and creates a real-time interactive art experience via immersive environments. Empirical data from artists and audience reveal modulations where musicians selectively embrace or reject algorithmic suggestions to preserve creative identity. The author concludes that decolonial potential requires redesigning tools or systems for cultural survivance, where technology becomes not merely a feedback environment but a site for decolonial praxis, challenging computational hegemony in digital ecosystems.

Related papers

• Co-policy: Responsive Human-Robot Co-Creation for Musical Performances [58.92814200971293]
  Co-policy is a framework for human-robot musical co-creation.<n>It separates semantic intent grounding, constrained musical variation, and visuomotor execution.<n>We show improved intent alignment, execution accuracy, and response frequency over diffusion-policy and ablated baselines.
  arXiv  Detail & Related papers  (2026-06-18T08:08:16Z)
• Towards Computational Chinese Paleography [0.0]
  Chinese paleography, the study of ancient Chinese writing, is undergoing a computational turn powered by artificial intelligence.<n>This position paper charts the trajectory of this emerging field, arguing that it is evolving from automating isolated visual tasks to creating integrated digital ecosystems for scholarly research.
  arXiv  Detail & Related papers  (2026-01-11T02:40:54Z)
• Supporting Creative Ownership through Deep Learning-Based Music Variation [4.71547360356314]
  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.
  arXiv  Detail & Related papers  (2025-09-30T06:18:36Z)
• Archiverse: an Approach for Immersive Cultural Heritage [1.7919147309612766]
  Digital technologies and tools have transformed the way we can study cultural heritage.<n>Mixed Reality solutions have enabled researchers to examine cultural objects and artifacts more precisely and from new perspectives.<n>Virtual Reality (VR) and eXtended Reality (XR) can serve as tools to recreate and visualize the remains of historical cultural heritage.
  arXiv  Detail & Related papers  (2025-07-25T15:26:18Z)
• AI-powered Contextual 3D Environment Generation: A Systematic Review [49.1574468325115]
  This study performs a systematic review of existing generative AI techniques for 3D scene generation.<n>By examining state-of-the-art approaches, it presents key challenges such as scene authenticity and the influence of textual inputs.
  arXiv  Detail & Related papers  (2025-06-05T15:56:28Z)
• Where is my Glass Slipper? AI, Poetry and Art [0.0]
  This literature review interrogates the intersections between artificial intelligence, poetry, and art.<n>It traces the development of computer-generated poetry from early template-based systems to generative models.<n>Review calls for a re-evaluation of creative processes that recognises the interdependence of technological innovation and human subjectivity.
  arXiv  Detail & Related papers  (2025-02-26T14:57:03Z)
• Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts [65.90535970515266]
  TimeTravel is a benchmark of 10,250 expert-verified samples spanning 266 distinct cultures across 10 major historical regions.<n>TimeTravel is designed for AI-driven analysis of manuscripts, artworks, inscriptions, and archaeological discoveries.<n>We evaluate contemporary AI models on TimeTravel, highlighting their strengths and identifying areas for improvement.
  arXiv  Detail & Related papers  (2025-02-20T18:59:51Z)
• 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)
• Prevailing Research Areas for Music AI in the Era of Foundation Models [10.245601263106844]
  As AI-generated and AI-augmented music become increasingly mainstream, many researchers in the music AI community may wonder: what research frontiers remain unexplored?<n>This paper outlines several key areas within music AI research that present significant opportunities for further investigation.
  arXiv  Detail & Related papers  (2024-09-14T09:06:43Z)
• On the Emergence of Symmetrical Reality [51.21203247240322]
  We introduce the symmetrical reality framework, which offers a unified representation encompassing various forms of physical-virtual amalgamations. We propose an instance of an AI-driven active assistance service that illustrates the potential applications of symmetrical reality.
  arXiv  Detail & Related papers  (2024-01-26T16:09:39Z)
• Exploring the Intersection of Complex Aesthetics and Generative AI for Promoting Cultural Creativity in Rural China after the Post-Pandemic Era [13.763608420284195]
  This paper explores using generative AI and aesthetics to promote cultural creativity in rural China amidst COVID-19's impact. The study finds artworks often fail to resonate locally, while reliance on external artists limits sustainability. Our approach involves training machine learning on subjective aesthetics to generate culturally relevant content.
  arXiv  Detail & Related papers  (2023-09-05T11:27:16Z)
• Pathway to Future Symbiotic Creativity [76.20798455931603]
  We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist to a Machine artist in its own right. In art creation, it is necessary for machines to understand humans' mental states, including desires, appreciation, and emotions, humans also need to understand machines' creative capabilities and limitations. We propose a novel framework for building future Machine artists, which comes with the philosophy that a human-compatible AI system should be based on the "human-in-the-loop" principle.
  arXiv  Detail & Related papers  (2022-08-18T15:12:02Z)
• Learning Robust Real-Time Cultural Transmission without Human Data [82.05222093231566]
  We provide a method for generating zero-shot, high recall cultural transmission in artificially intelligent agents. Our agents succeed at real-time cultural transmission from humans in novel contexts without using any pre-collected human data. This paves the way for cultural evolution as an algorithm for developing artificial general intelligence.
  arXiv  Detail & Related papers  (2022-03-01T19:32:27Z)

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.