A Benchmarking Initiative for Audio-domain Music Generation using the FreeSound Loop Dataset

This paper proposes a new benchmark task for generating musical passages in the audio domain by using the drum loops from the FreeSound Loop Dataset, which are publicly re-distributable. Moreover, we use a larger collection of drum loops from Looperman to establish four model-based objective metrics for evaluation, releasing these metrics as a library for quantifying and facilitating the progress of musical audio generation. Under this evaluation framework, we benchmark the performance of three recent deep generative adversarial network (GAN) models we customize to generate loops, including StyleGAN, StyleGAN2, and UNAGAN. We also report a subjective evaluation of these models. Our evaluation shows that the one based on StyleGAN2 performs the best in both objective and subjective metrics