# Domain Adaptation for Contrastive Audio-Language Models

arXiv: https://arxiv.org/abs/2402.09585 · HTML: https://mlsanity.com/paper/2402.09585/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2024.

## Built on by (most cited first, 8 of 8)

- [Taming Data and Transformers for Audio Generation](https://mlsanity.com/paper/2406.19388/) (2024)
- [PALM: Few-Shot Prompt Learning for Audio Language Models](https://mlsanity.com/paper/2409.19806/) (2024)
- [SLAM-LLM: A Modular, Open-Source Multimodal Large Language Model Framework and Best Practice for Speech, Language, Audio and Music Processing](https://mlsanity.com/paper/2601.09385/) (2026)
- [A sound description: Exploring prompt templates and class descriptions to enhance zero-shot audio classification](https://mlsanity.com/paper/2409.13676/) (2024)
- [Domain Adaptation Method and Modality Gap Impact in Audio-Text Models for Prototypical Sound Classification](https://mlsanity.com/paper/2506.04376/) (2025)
- [Multiple Consistency-guided Test-Time Adaptation for Contrastive Audio-Language Models with Unlabeled Audio](https://mlsanity.com/paper/2412.17306/) (2024)
- [EMO-TTA: Improving Test-Time Adaptation of Audio-Language Models for Speech Emotion Recognition](https://mlsanity.com/paper/2509.25495/) (2025)
- [CALM: Class-Conditional Sparse Attention Vectors for Large Audio-Language Models](https://mlsanity.com/paper/2602.07077/) (2026)

## Abstract (arXiv)

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.