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.

翻译：高质量的训练数据集对神经网络的性能至关重要。然而，音频领域仍缺乏大规模、强标签且单源的声音事件数据集。FSD50K数据集尽管规模较大且开放，但其中包含相当比例的多源样本，背景干扰或事件重叠可能限制数据的实用性。为解决这一挑战，我们提出了一种专为大规模开放音频语料库设计的数据策展框架。该方法利用生成式扩散模型合成纯净的单类事件，以构建受控的混合噪声用于监督。随后，我们采用预训练音频编码器与判别性分类器相结合，来自动识别并过滤多源样本。实验表明，该框架在人工专家策展的测试集上取得了强劲性能。最终，我们发布了FSD50K-Solo，它是通过我们的方法识别的单源音频样本对FSD50K进行模型策展的子集。除FSD50K外，该方法为策展开源音频语料库建立了一种可扩展的范式。