{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/improving-audio-language-learning-with-mixgen","title":"Exploring Train and Test-Time Augmentations for Audio-Language Learning","arxiv_id":"2210.17143","date":"2022-10-31","proceeding":null,"authors":["Eungbeom Kim","Jinhee Kim","Yoori Oh","KyungSu Kim","Minju Park","Jaeheon Sim","Jinwoo Lee","Kyogu Lee"],"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.","url_abs":"https://arxiv.org/abs/2210.17143v2","url_pdf":"https://arxiv.org/pdf/2210.17143v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"audio-captioning","task_name":"Audio captioning"},{"task_slug":"audio-to-text-retrieval","task_name":"Audio to Text Retrieval"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"text-retrieval","task_name":"Text Retrieval"},{"task_slug":"text-to-audio-retrieval","task_name":"Text to Audio Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/audio-captioning-on-audiocaps","task":"Audio captioning","dataset":"AudioCaps","model":"AL-MixGen","rank_in_archive_order":13,"of":18,"metrics":{"CIDEr":"0.755","SPICE":"0.177","SPIDEr":"0.466"},"uses_additional_data":false},{"leaderboard":"/sota/text-to-audio-retrieval-on-audiocaps","task":"Text to Audio Retrieval","dataset":"AudioCaps","model":"AL-MixGen + Multi-TTA","rank_in_archive_order":6,"of":11,"metrics":{"R@1":"34.7","R@10":"83.3"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2210.17143","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}