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arXiv · 2603.25767

Unlocking Strong Supervision: A Data-Centric Study of General-Purpose Audio Pre-Training Methods

Xuanru Zhou·Yiwen Shao·Wei-Cheng Tseng·Dong Yu
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Abstract

Current audio pre-training seeks to learn unified representations for broad audio understanding tasks, but it remains fragmented and is fundamentally bottlenecked by its reliance on weak, noisy, and scale-limited labels. Drawing lessons from vision's foundational pre-training blueprint, we argue that the audio field must first establish its own large-scale, strong supervision framework. We introduce a new data-centric pipeline that leverages a high-fidelity captioner to create SOTA-quality captions and the first Unified Tag System (UTS) that bridges speech, music, and environmental sounds. We then conduct a systematic comparative study of different pre-training objectives on these strong source data. Our experiments suggest that data quality and coverage are the primary drivers of performance, while the choice of objective dictates downstream task specialization.

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Xuanru Zhou, Yiwen Shao, Wei-Cheng Tseng, Dong Yu. 2026-03-26. Unlocking Strong Supervision: A Data-Centric Study of General-Purpose Audio Pre-Training Methods. https://arxiv.org/abs/2603.25767

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