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ABSTRACT Data quality issues such as off-topic samples, near duplicates, and label errors often limit the performance of audio-based systems. This approach leverages self-supervised audio representations to identify common data quality issues, creating ranked review lists that surface distinct issues within a single, unified process. The method is benchmarked on the ESC-50, GTZAN, and a proprietary industrial dataset, using both synthetic and naturally occurring corruptions. The results demonstrate that this framework achieves state-of-the-art ranking performance, often outperforming issue-specific baselines and enabling significant annotation savings by efficiently guiding human review. Index T erms-- Data quality, dataset auditing, representation learning, near-duplicate detection, label errors 1. INTRODUCTION High-stakes audio applications, from predictive maintenance and safety monitoring to large-scale media search, depend on data that is abundant and trustworthy [1, 2, 3].