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Training AI to identify audio effects in speech is now possible, thanks to a new dataset that precisely labels how effects are applied.
By reinterpreting unsupervised anomaly detection through the lens of Retrieval-Augmented Generation, RAID achieves state-of-the-art performance by using retrieved normal samples to guide noise suppression in anomaly map generation.
