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This paper proposes a new benchmark task for generat-ing musical passages inthe audio domain by using thedrum loops from the FreeSound Loop Dataset, whicharepublicly re-distributable. Moreover, we use a larger col-lection of drumloops from Looperman to establish fourmodel-based objective metrics forevaluation, releasingthese metrics as a library for quantifying andfacilitatingthe progress of musical audio generation. Under this eval-uationframework, we benchmark the performance of threerecent deep generativeadversarial network (GAN) mod-els we customize to generate loops, includingStyleGAN,StyleGAN2, and UNAGAN. We also report a subjectiveevaluation of thesemodels. Our evaluation shows that theone based on StyleGAN2 performs the bestin both objec-tive and subjective metrics.

A Benchmarking Initiative for Audio-Domain Music Generation Using the Freesound Loop Dataset