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Audio-language models struggle significantly with hidden evaluation tasks, with accuracy plummeting by nearly 12 percentage points on average, highlighting the challenge of true audio comprehension.
Achieving nearly 4% accuracy gains in audio classification tasks with a novel automatic annotation pipeline could transform data scarcity challenges in domestic environments.
