Domain Adaptation for Contrastive Audio-Language Models
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910536266940416 |
|---|---|
| author | Deshmukh, Soham Singh, Rita Raj, Bhiksha |
| author_facet | Deshmukh, Soham Singh, Rita Raj, Bhiksha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09585 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Domain Adaptation for Contrastive Audio-Language Models Deshmukh, Soham Singh, Rita Raj, Bhiksha Sound Audio and Speech Processing 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. |
| title | Domain Adaptation for Contrastive Audio-Language Models |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2402.09585 |