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arXiv · 2108.01576

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

Tun-Min Hung·Bo-Yu Chen·Yen-Tung Yeh·Yi-Hsuan Yang
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Abstract

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.

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Tun-Min Hung, Bo-Yu Chen, Yen-Tung Yeh, Yi-Hsuan Yang. 2021-08-03. A Benchmarking Initiative for Audio-Domain Music Generation Using the Freesound Loop Dataset. https://arxiv.org/abs/2108.01576

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