User Acceptance and Perceived Value of AI Virtual Singers in Music Production

This dataset and accompanying materials are part of a study investigating the user acceptance and perceived value of AI-powered virtual singers in multilingual music production. The research, conducted with 378 global music practitioners and educators, explores key factors influencing the adoption of AI virtual singers, including technical familiarity, simulation fidelity, cost efficiency, and the balance between functional and cultural utility. Through a mixed-methods approach combining quantitative surveys and qualitative sentiment analysis, the study provides insights into how AI virtual singers are perceived by music producers and educators, highlighting both opportunities and challenges for their integration into the music production process.The dataset includes responses from a multilingual questionnaire measuring user perceptions, as well as the Python analysis code used for sentiment analysis of open-ended responses. Key findings suggest that technical familiarity significantly correlates with acceptance, and while AI virtual singers offer cost and workflow advantages, limitations in emotional expressiveness remain a major barrier to wider adoption. The research also identifies the importance of software optimization for different occupational groups and recommends further development in affective synthesis models for improved emotional authenticity in virtual singers.