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AImoclips: A Benchmark for Evaluating Emotion Conveyance in Text-to-Music Generation
Gyehun Go, Satbyul Han, Ahyeon Choi, Eunjin Choi, Juhan Nam, Jeong Mi Park
https://arxiv.org/abs/2509.00813

AImoclips: A Benchmark for Evaluating Emotion Conveyance in Text-to-Music Generation
Recent advances in text-to-music (TTM) generation have enabled controllable and expressive music creation using natural language prompts. However, the emotional fidelity of TTM systems remains largely underexplored compared to human preference or text alignment. In this study, we introduce AImoclips, a benchmark for evaluating how well TTM systems convey intended emotions to human listeners, covering both open-source and commercial models. We selected 12 emotion intents spanning four quadrants …