Audio-Language Models (ALM) aim to be general-purpose audio models by providing zero-shot capabilities at test time. The zero-shot performance of ALM improves by using suitable text prompts for each domain. The text prompts are usually hand-crafted through an ad-hoc process and lead to a drop in ALM generalization and out-of-distribution performance. Existing approaches to improve domain performance, like few-shot learning or fine-tuning, require access to annotated data and iterations of training. Therefore, we propose a test-time domain adaptation method for ALMs that does not require access to annotations. Our method learns a domain vector by enforcing consistency across augmented views of the testing audio. We extensively evaluate our approach on 12 downstream tasks across domains. With just one example, our domain adaptation method leads to 3.2% (max 8.4%) average zero-shot performance improvement. After adaptation, the model still retains the generalization property of ALMs.

翻译：音频语言模型（ALM）旨在通过提供测试时的零样本能力来成为通用音频模型。通过为每个领域使用合适的文本提示，ALM的零样本性能得以提升。文本提示通常通过临时的手工方式生成，导致ALM的泛化能力和分布外性能下降。现有的提升领域性能的方法，如少样本学习或微调，需要访问标注数据和训练迭代。因此，我们提出一种无需访问标注数据的ALM测试时领域自适应方法。该方法通过强制测试音频的增强视图之间保持一致性来学习领域向量。我们在跨领域的12个下游任务上广泛评估了该方法。仅使用一个示例，我们的领域自适应方法平均零样本性能提升3.2%（最高8.4%）。自适应后，模型仍保留ALM的泛化特性。