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arXiv · 2605.13931

FSD50K-Solo: Automated Curation of Single-Source Sound Events

Ningyuan Yang·Sile Yin·Li-Chia Yang·Bryce Irvin·Xiao Quan·Marko Stamenovic·Shuo Zhang
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Abstract

High-quality training datasets are essential for the performance of neural networks. However, the audio domain still lacks a large-scale, strongly-labeled, and single-source sound event dataset. The FSD50K dataset, despite being relatively large and open, contains a considerable fraction of multi-source samples where background interference or overlapping events could limit the usefulness of the data. To address this challenge, we introduce a data curation framework designed for large-scale open audio corpora. Our approach leverages a generative diffusion model to synthesize clean single-class events to construct controlled noisy mixtures for supervision. We subsequently employ a pre-trained audio encoder coupled with a discriminative classifier to automatically identify and filter out multi-source samples. Experiments show that our framework achieves strong performance on a human expert-curated test set. Finally, we release FSD50K-Solo, a model-curated subset of FSD50K containing single-source audio samples identified by our method. Beyond FSD50K, our method establishes a scalable paradigm for curating open source audio corpora.

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Ningyuan Yang, Sile Yin, Li-Chia Yang, Bryce Irvin, Xiao Quan, Marko Stamenovic, Shuo Zhang. 2026-05-27. FSD50K-Solo: Automated Curation of Single-Source Sound Events. https://arxiv.org/abs/2605.13931

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