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

arXiv: https://arxiv.org/abs/2108.01576 · HTML: https://mlsanity.com/paper/2108.01576/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2021.

## Built on by (most cited first, 4 of 4)

- [JukeDrummer: Conditional Beat-aware Audio-domain Drum Accompaniment Generation via Transformer VQ-VAE](https://mlsanity.com/paper/2210.06007/) (2022)
- [Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?](https://mlsanity.com/paper/2201.06346/) (2022)
- [IteraTTA: An interface for exploring both text prompts and audio priors in generating music with text-to-audio models](https://mlsanity.com/paper/2307.13005/) (2023)
- [Comparision Of Adversarial And Non-Adversarial LSTM Music Generative Models](https://mlsanity.com/paper/2211.00731/) (2022)

## Abstract (arXiv)

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.