会议信息

EvoMUSART 2027: International Conference on Computational Intelligence in Music, Sound, Art and Design

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截稿日期:
2026-11-01 已截稿
通知日期:
2027-01-10
会议日期:
2027-03-31
会议地点:
Mainz, Germany
届数:
主办方:
ICORE: C   浏览: 16608   关注: 0   参加: 0
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EvoMUSART
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会伴指数 (CP-I)

64.8 / 100
全站第 459 名 / 共 5,771 个会议 · 前 8%

人工智能与机器学习 第 35 / 755

学术认可 (35%)
58
投稿选择性 (20%)
74
会议传承 (20%)
76
社区关注 (10%)
17
资料公开度 (15%)
85

用到的输入: 收录等级：ICORE C · 录用率：33.8%（有记录的 5 届的均值） · 有据可查的届次：16 · 过去 24 个月打开过本页的研究者：9 人

公开资料里还缺: 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 100% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.2 · 算于 2026-11-09

征稿

EvoMUSART 2027 (International Conference on Computational Intelligence in Music, Sound, Art and Design) is a ICORE C conference held in Mainz, Germany on 2027-03-31. The paper submission deadline is 2026-11-01. Acceptance notifications are sent on 2027-01-10.

The 16th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMusArt) will take place on 31 March – 2 April, 2027, as part of the evostar event. EvoMusArt webpage: https://www.evostar.org/2027/evomusart/ Submission deadline: 1 November 2026 Conference: 31 March – 2 April 2027 EvoMusArt is a multidisciplinary conference that brings together researchers working on the application of Artificial Neural Networks, Evolutionary Computation, Swarm Intelligence, Cellular Automata, Artificial Life (Alife), Generative AI, Foundation Models, and other Artificial Intelligence (AI) techniques in creative and artistic fields such as Visual Art, Music, Architecture, Video, Digital Games, Poetry, Design, and Interactive Media. The conference provides a forum for presenting and discussing novel research, artistic practices, systems, and applications that explore computational creativity, human-AI co-creation, and emerging creative technologies. Submissions must be at most 14 pages long, excluding references, in Springer Lecture Notes in Computer Science (LNCS) format. Each submission must be anonymised for a double-blind review process. Accepted papers will be presented orally or as posters at the event and included in the EvoMusArt proceedings published by Springer Nature in a dedicated volume of the LNCS series. Submissions should address the use of AI techniques (e.g. Evolutionary Computation, Artificial Neural Networks, Alife, Machine Learning (ML), Deep Learning, and Swarm Intelligence) in creative and artistic domains, including art, music, design, architecture, and related fields. Topics of interest span generation, automation, computer-aided creativity and creativity support tools, and theoretical aspects of computational creativity, including but not limited to: — Systems that create drawings, images, animations, videos, sculptures, poetry, text, designs, webpages, buildings, virtual environments, and other creative artefacts; — Systems that create musical pieces, sounds, instruments, voices, sound effects, soundscapes, and multimodal artistic experiences; — Systems that create artefacts such as game content, architecture, furniture, industrial products, and digital experiences based on aesthetic and functional criteria; — Generative AI, foundation models, large language models (LLMs), multimodal models, and diffusion-based creative systems; — Computational creativity in digital games, interactive storytelling, and narrative generation; — Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Extended Reality (XR) for artistic and creative applications, including immersive art installations and interactive creative environments; — Robotic-based Evolutionary Art and Music; — Embodied AI, tangible and wearable interfaces, and smart environments for creative applications; — Digital fabrication, 3D printing, and computational approaches to physical artefact creation; — Other artificial, generative, evolutionary, or biologically inspired techniques in the fields of Computer Music, Computer Art, Design, and Digital Culture; — Techniques for automatic fitness assignment; — AI agents and autonomous creative systems; — Systems in which analysis or interpretation of artworks is combined with AI techniques to generate novel artefacts; — Systems that use AI for the analysis, understanding, restoration, preservation, or curation of artistic and cultural heritage resources; — Human-AI co-creation, mixed-initiative systems, intelligent interfaces, and AI-based creativity support tools; — New ways of integrating users and audiences into the evolutionary, generative, or creative cycle; — User-centred evaluation of creative AI systems and creative experiences; — Analysis and evaluation of the artistic potential of biologically inspired and AI-based creative systems, their creative processes, and resulting artefacts; — Collaborative, distributed, and multi-user creative systems, networked art environments, and collective computational creativity; — Contextualisation of Creative AI in cultural, economic, social, political, legal, ethical, ecological, and sustainability-related discourse. — Computational Aesthetics, Experimental Aesthetics, Emotional Response, Engagement, Surprise, Novelty, and Meaning; — Representation techniques and creative knowledge representations; — Explainability, transparency, authorship, ownership, and ethics in Creative AI; — Surveys of the current state of the art; identification of strengths and weaknesses; comparative analyses and taxonomies; — Validation and evaluation methodologies for creative systems and generated artefacts; — Studies on the applicability of these techniques to related domains; — New models designed to promote creativity through evolutionary computation, Alife, ML, and hybrid approaches. More information on the submission process and topics: https://www.evostar.org/2027/evomusart/ Papers published in previous editions of EvoMusArt: https://evomusart-index.dei.uc.pt The EvoMUSART 2027 organisers Sérgio M. Rebelo Patrick Donnelly Nereida Rodríguez-Fernández (publication chair)
由 Sérgio M Rebelo 最后更新于

录用率

平均录用率: 35.8% 6 年间 (2019–2026). 官方

年份提交数录用数录用率(%)
2026581322.4%
2023552036.4%
2022511835.3%
2021662436.4%
2020311238.7%
2019241145.8%

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