(a) Confusion matrix for the
music pieces generated by
Moûsai. (y-axis: true genre;
x-axis: inferred genre.)

(b) Confusion matrix for the
music pieces generated by
the Riffusion model.

Figure 5: For the text-music relevance check, we ask
the annotators to infer the ground-truth genres of the
generated music by (a) our model and (b) the Riffusion
model. The darker diagonal means better results. Note
that each matrix adds up to 120, corresponding to 40
samples per model annotated by three perceivers each.

TEXT2MUSIC dataset: electronic, hip hop, metal,
and pop. See Appendix C.1 for the entire list of
prompts. Inspired by the two-alternative forced
choice (2AFC) experiment design, we design a
four-alternative forced choice (4AFC) paradigm,
where the annotators need to categorize each mu-
sic sample into exactly one of the four provided
categories. See annotation details in Appendix C.1.
MOUSAI