  This paper proposes a new benchmark task for generat-ing musical passages in
the audio domain by using thedrum loops from the FreeSound Loop Dataset, which
arepublicly re-distributable. Moreover, we use a larger col-lection of drum
loops from Looperman to establish fourmodel-based objective metrics for
evaluation, releasingthese metrics as a library for quantifying and
facilitatingthe progress of musical audio generation. Under this eval-uation
framework, we benchmark the performance of threerecent deep generative
adversarial network (GAN) mod-els we customize to generate loops, including
StyleGAN,StyleGAN2, and UNAGAN. We also report a subjectiveevaluation of these
models. Our evaluation shows that theone based on StyleGAN2 performs the best
in both objec-tive and subjective metrics.
