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Machine generation of symbolic music and digital audio are hot topics but there have been relatively few digital musical instruments that integrate generative AI. Present musical AI tools are not artist centred and do not support…

声音 · 计算机科学 2026-04-28 Charles Patrick Martin

We present Hookpad Aria, a generative AI system designed to assist musicians in writing Western pop songs. Our system is seamlessly integrated into Hookpad, a web-based editor designed for the composition of lead sheets: symbolic music…

声音 · 计算机科学 2025-02-13 Chris Donahue , Shih-Lun Wu , Yewon Kim , Dave Carlton , Ryan Miyakawa , John Thickstun

This study proposes a system designed to enumerate the process of collaborative composition among humans, using automatic music composition technology. By integrating multiple Recurrent Neural Network (RNN) models, the system provides an…

声音 · 计算机科学 2024-03-07 So Hirawata , Noriko Otani

Autoregressive models are now capable of generating high-quality minute-long expressive MIDI piano performances. Even though this progress suggests new tools to assist music composition, we observe that generative algorithms are still not…

声音 · 计算机科学 2021-07-14 Gaëtan Hadjeres , Léopold Crestel

We present a framework for real-time human-AI musical co-performance, in which a latent diffusion model generates instrumental accompaniment in response to a live stream of context audio. The system combines a MAX/MSP front-end-handling…

声音 · 计算机科学 2026-04-10 Tornike Karchkhadze , Shlomo Dubnov

In order to collaborate and co-create with humans, an AI system must be capable of both reactive and anticipatory behavior. We present a case study of such a system in the domain of musical improvisation. We consider a duo consisting of a…

人工智能 · 计算机科学 2019-06-06 Oscar Thörn , Peter Fögel , Peter Knudsen , Luis de Miranda , Alessandro Saffiotti

Recent advances in generative AI have made music generation a prominent research focus. However, many neural-based models rely on large datasets, raising concerns about copyright infringement and high-performance costs. In contrast, we…

声音 · 计算机科学 2025-11-24 Callie C. Liao , Duoduo Liao , Ellie L. Zhang

Most digital music tools emphasize precision and control, but often lack support for tactile, improvisational workflows grounded in environmental interaction. Lumia addresses this by enabling users to "compose through looking"--transforming…

人机交互 · 计算机科学 2025-12-22 Chung-Ta Huang , Connie Cheng , Vealy Lai

Creating music is iterative, requiring varied methods at each stage. However, existing AI music systems fall short in orchestrating multiple subsystems for diverse needs. To address this gap, we introduce Loop Copilot, a novel system that…

声音 · 计算机科学 2024-09-02 Yixiao Zhang , Akira Maezawa , Gus Xia , Kazuhiko Yamamoto , Simon Dixon

This paper presents a deep reinforcement learning algorithm for online accompaniment generation, with potential for real-time interactive human-machine duet improvisation. Different from offline music generation and harmonization, online…

机器学习 · 计算机科学 2020-02-11 Nan Jiang , Sheng Jin , Zhiyao Duan , Changshui Zhang

Musical professionals who produce material for non-musical stakeholders often face communication challenges in the early ideation stage. Expressing musical ideas can be difficult, especially when domain-specific vocabulary is lacking. This…

人机交互 · 计算机科学 2023-03-03 Sebastian Löbbers , Mathieu Barthet , György Fazekas

We present a study that explores the role of user-centred design in developing Generative AI (GenAI) tools for music composition. Through semi-structured interviews with professional composers, we gathered insights on a novel generative…

声音 · 计算机科学 2024-12-17 Eleanor Row , György Fazekas

The rapid development of musical AI technologies has expanded the creative potential of various musical activities, ranging from music style transformation to music generation. However, little research has investigated how musical AIs can…

人机交互 · 计算机科学 2024-04-16 Jingjing Sun , Jingyi Yang , Guyue Zhou , Yucheng Jin , Jiangtao Gong

Recent advances in generative artificial intelligence (AI) have created models capable of high-quality musical content generation. However, little consideration is given to how to use these models for real-time or cooperative jamming…

Existing AI-generated dance methods primarily train on motion capture data from solo dance performances, but a critical feature of dance in nearly any genre is the interaction of two or more bodies in space. Moreover, many works at the…

机器学习 · 计算机科学 2025-03-07 Zixuan Wang , Luis Zerkowski , Ilya Vidrin , Mariel Pettee

Music is an inherently social activity that allows people to share experiences and feel connected with one another. There has been little progress in designing artificial partners exhibiting a similar social experience as playing with…

社会与信息网络 · 计算机科学 2024-02-15 Dobromir Dotov , Dante Camarena , Zack Harris , Joanna Spyra , Pietro Gagliano , Laurel Trainor

Our research explores the development and application of musical agents, human-in-the-loop generative AI systems designed to support music performance and improvisation within co-creative spaces. We introduce MACAT and MACataRT, two…

多智能体系统 · 计算机科学 2025-02-04 Keon Ju M. Lee , Philippe Pasquier

This paper explores the integration of AI technologies into the artistic workflow through the creation of Studies for, a generative sound installation developed in collaboration with sound artist Evala…

声音 · 计算机科学 2025-11-03 Chihiro Nagashima , Akira Takahashi , Zhi Zhong , Shusuke Takahashi , Yuki Mitsufuji

Collaboration is built on trust, and establishing trust with a creative Artificial Intelligence is difficult when the decision process or internal state driving its behaviour isn't exposed. When human musicians improvise together, a number…

Recent advances in generative AI music have resulted in new technologies that are being framed as co-creative tools for musicians with early work demonstrating their potential to add to music practice. While the field has seen many valuable…

人机交互 · 计算机科学 2025-02-14 Stephen James Krol , Maria Teresa Llano Rodriguez , Miguel Loor Paredes
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