AI-Assisted Music Production: A User Study on Text-to-Music Models
AI-Assisted Music Production: A User Study on Text-to-Music Models
Text-to-music models have revolutionized the creative landscape, offering new possibilities for music creation. Yet their integration into musicians workflows remains underexplored. This paper presents a case study on how TTM models impact music production, based on a user study of their effect on producers creative workflows. Participants produce tracks using a custom tool combining TTM and source separation models. Semi-structured interviews and thematic analysis reveal key challenges, opportunities, and ethical considerations. The findings offer insights into the transformative potential of TTMs in music production, as well as challenges in their real-world integration.
Accepted at 17th International Symposium on Computer Music Multidisciplinary Research (CMMR 25)
Signal Processing (eess.SP), FOS: Computer and information sciences, Sound (cs.SD), Human-Computer Co-Creativity, Text-to-Music, Human-AI Interaction, Machine Learning (cs.LG), Machine Learning, Sound, Audio and Speech Processing (eess.AS), Signal Processing, FOS: Electrical engineering, electronic engineering, information engineering, Generative Models, Audio and Speech Processing
Signal Processing (eess.SP), FOS: Computer and information sciences, Sound (cs.SD), Human-Computer Co-Creativity, Text-to-Music, Human-AI Interaction, Machine Learning (cs.LG), Machine Learning, Sound, Audio and Speech Processing (eess.AS), Signal Processing, FOS: Electrical engineering, electronic engineering, information engineering, Generative Models, Audio and Speech Processing
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