Visually-Aware Audio Captioning With Adaptive Audio-Visual Attention

Xubo Liu, Qiushi Huang, Xinhao Mei, Haohe Liu, Qiuqiang Kong, Jianyuan Sun, Shengchen Li, Tom Ko, Yu Zhang, Lilian H. Tang, Mark D. Plumbley, Volkan Kılıç, Wenwu Wang

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)

Abstract

Audio captioning aims to generate text descriptions of audio clips. In the real world, many objects produce similar sounds. How to accurately recognize ambiguous sounds is a major challenge for audio captioning. In this work, inspired by inherent human multimodal perception, we propose visually-aware audio captioning, which makes use of visual information to help the description of ambiguous sounding objects. Specifically, we introduce an off-the-shelf visual encoder to extract video features and incorporate the visual features into an audio captioning system. Furthermore, to better exploit complementary audio-visual contexts, we propose an audio-visual attention mechanism that adaptively integrates audio and visual context and removes the redundant information in the latent space. Experimental results on AudioCaps, the largest audio captioning dataset, show that our proposed method achieves state-of-the-art results on machine translation metrics.
Original languageEnglish
Pages2838-2842
Number of pages5
DOIs
Publication statusPublished - 31 Aug 2023
Event24th International Speech Communication Association, Interspeech 2023 - Dublin, Ireland
Duration: 20 Aug 202324 Aug 2023

Conference

Conference24th International Speech Communication Association, Interspeech 2023
Country/TerritoryIreland
CityDublin
Period20/08/2324/08/23

Keywords

  • Audio captioning
  • attention mechanism
  • audio-visual learning
  • multimodal learning

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