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arXiv · 2210.17143

Exploring Train and Test-Time Augmentations for Audio-Language Learning

Eungbeom Kim·Jinhee Kim·Yoori Oh·Kyungsu Kim·Minju Park·Jaeheon Sim·Jinwoo Lee·Kyogu Lee
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Abstract

In this paper, we aim to unveil the impact of data augmentation in audio-language multi-modal learning, which has not been explored despite its importance. We explore various augmentation methods at not only train-time but also test-time and find out that proper data augmentation can lead to substantial improvements. Specifically, applying our proposed audio-language paired augmentation PairMix, which is the first multi-modal audio-language augmentation method, outperforms the baselines for both automated audio captioning and audio-text retrieval tasks. To fully take advantage of data augmentation, we also present multi-level test-time augmentation (Multi-TTA) for the test-time. We successfully incorporate the two proposed methods and uni-modal augmentations and achieve 47.5 SPIDEr on audio captioning, which is an 18.2% relative increase over the baseline. In audio-text retrieval, the proposed methods also show an improvement in performance as well.

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Eungbeom Kim, Jinhee Kim, Yoori Oh, Kyungsu Kim, Minju Park, Jaeheon Sim, Jinwoo Lee, Kyogu Lee. 2023-05-23. Exploring Train and Test-Time Augmentations for Audio-Language Learning. https://arxiv.org/abs/2210.17143

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