EDBT 2026 Demo / reviewers in the wild / expert
Yvan Grabit
dblp:354/1187
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Human-AI interaction · 44% Usability and user experience research · 44% Games and playful interaction · 13% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-AI interaction
human-AI co-creation |
0.7 | 1 | 2023 | Evaluating Human-AI Interaction via Usability, User Experience and Acceptance Measures for MMM-C: A Creative AI System for Music Composition · IJCAI 2023 |
Usability and user experience research
technology acceptance |
0.7 | 1 | 2023 | Evaluating Human-AI Interaction via Usability, User Experience and Acceptance Measures for MMM-C: A Creative AI System for Music Composition · IJCAI 2023 |
Methods — techniques the papers use, named apart from their topics
mixed-methods study · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Evaluating Human-AI Interaction via Usability, User Experience and Acceptance Measures for MMM-C: A Creative AI System for Music CompositionabstractWith the rise of artificial intelligence (AI), there has been increasing interest in human-AI co-creation in a variety of artistic domains including music as AI-driven systems are frequently able to generate human-competitive artifacts. Now, the implications of such systems for the musical practice are being investigated. This paper reports on a thorough evaluation of the user adoption of the Multi-Track Music Machine (MMM) as a minimal co-creative AI tool for music composers. To do this, we integrate MMM into Cubase, a popular Digital Audio Workstation (DAW), by producing a "1-parameter" plugin interface named MMM-Cubase, which enables human-AI co-composition. We conduct a 3-part mixed method study measuring usability, user experience and technology acceptance of the system across two groups of expert-level composers: hobbyists and professionals. Results show positive usability and acceptance scores. Users report experiences of novelty, surprise and ease of use from using the system, and limitations on controllability and predictability of the interface when generating music. Findings indicate no significant difference between the two user groups. Renaud Bougueng Tchemeube, Jeff Ens, Cale Plut, Philippe Pasquier, Maryam Safi, Yvan Grabit, Jean-Baptiste Rolland |
IJCAI | 6 |