  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.
