Adapting Language-Audio Models as Few-Shot Audio Learners

Jinhua Liang; Xubo Liu; Haohe Liu; Huy Phan; Emmanouil Benetos; Mark D. Plumbley; Wenwu Wang

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arXiv · 2305.17719

Adapting Language-Audio Models as Few-Shot Audio Learners

Jinhua Liang·Xubo Liu·Haohe Liu·Huy Phan·Emmanouil Benetos·Mark D. Plumbley·Wenwu Wang

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Abstract

We presented the Treff adapter, a training-efficient adapter for CLAP, to boost zero-shot classification performance by making use of a small set of labelled data. Specifically, we designed CALM to retrieve the probability distribution of text-audio clips over classes using a set of audio-label pairs and combined it with CLAP's zero-shot classification results. Furthermore, we designed a training-free version of the Treff adapter by using CALM as a cosine similarity measure. Experiments showed that the proposed Treff adapter is comparable and even better than fully-supervised methods and adaptation methods in low-shot and data-abundant scenarios. While the Treff adapter shows that combining large-scale pretraining and rapid learning of domain-specific knowledge is non-trivial for obtaining generic representations for few-shot learning, it is still limited to audio classification tasks. In the future, we will explore how to use audio-language models in diverse audio domains.

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BibTeX RIS

Jinhua Liang, Xubo Liu, Haohe Liu, Huy Phan, Emmanouil Benetos, Mark D. Plumbley, Wenwu Wang. 2023-05-28. Adapting Language-Audio Models as Few-Shot Audio Learners. https://arxiv.org/abs/2305.17719

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