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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 6

Automatic audio captioning based on multi-modal representation learning

Tan Liwen1
Zhou Yi1
Liu Yin1
Cao Yin2
1. School of Communication & Information Engineering, Chongqing University of Posts & Telecommunications, Chongqing 400065, China
2. Dept. of Intelligent Science, Xi'an Jiaotong-Liverpool University, Suzhou Jangsu 215000, China
DOI: 10.19734/j.issn.1001-3695.2024.10.0465

Abstract

Modality discrepancies have perpetually posed significant challenges for the application of Automated Audio Captioning (AAC) and across all multi-modal research domains. Facilitating models in comprehending text information plays a pivotal role in establishing a seamless connection between the two modalities of text and audio. Recent studies have concentrated on narrowing the disparity between these two modalities via contrastive learning. However, bridging the gap between them merely by employing a simple contrastive loss function is challenging. In order to reduce the influence of modal differences and enhance the utilization of the model for the two modal features, this research proposes SimTLNet, an audio captioning method based on multi-modal representation learning by introducing a novel representation module, TRANSLATOR, constructing a twin representation structure, and jointly optimizing the model weights through contrastive learning and momentum updates, which enables the model to concurrently learn the common high-dimensional semantic information between the audio and text modalities. The proposed method achieved 0.251, 0.782, 0.480 for METEOR, CIDEr, and SPIDEr-FL on AudioCaps dataset and 0.187, 0.475, 0.303 for Clotho V2 dataset, respectivelyt, which are comparable with state-of-the-art methods and effectively bridge the difference between the two modalities.

Foundation Support

国家自然科学基金资助项目(62301096)
重庆市自然科学基金资助项目(CSTB2023NSCQ-MSX0659)
国家重点研究与发展(R&D)计划资助项目(2024QY2630)
西交利物浦大学资助项目(RDF-22-01-084)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.10.0465
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 6

Publish History

[2025-03-10] Accepted Paper

Cite This Article

谭力文, 周翊, 柳银, 等. 基于多模态表征学习的自动音频字幕方法 [J]. 计算机应用研究, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0465. (Tan Liwen, Zhou Yi, Liu Yin, et al. Automatic audio captioning based on multi-modal representation learning [J]. Application Research of Computers, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0465. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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