{"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/audiolog-llms-powered-long-audio-logging-with","title":"AudioLog: LLMs-Powered Long Audio Logging with Hybrid Token-Semantic Contrastive Learning","arxiv_id":"2311.12371","date":"2023-11-21","proceeding":null,"authors":["Jisheng Bai","Han Yin","Mou Wang","Dongyuan Shi","Woon-Seng Gan","Jianfeng Chen","Susanto Rahardja"],"abstract":"Previous studies in automated audio captioning have faced difficulties in accurately capturing the complete temporal details of acoustic scenes and events within long audio sequences. This paper presents AudioLog, a large language models (LLMs)-powered audio logging system with hybrid token-semantic contrastive learning. Specifically, we propose to fine-tune the pre-trained hierarchical token-semantic audio Transformer by incorporating contrastive learning between hybrid acoustic representations. We then leverage LLMs to generate audio logs that summarize textual descriptions of the acoustic environment. Finally, we evaluate the AudioLog system on two datasets with both scene and event annotations. Experiments show that the proposed system achieves exceptional performance in acoustic scene classification and sound event detection, surpassing existing methods in the field. Further analysis of the prompts to LLMs demonstrates that AudioLog can effectively summarize long audio sequences. To the best of our knowledge, this approach is the first attempt to leverage LLMs for summarizing long audio sequences.","url_abs":"https://arxiv.org/abs/2311.12371v2","url_pdf":"https://arxiv.org/pdf/2311.12371v2.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":[{"paper_slug":"audiolog-llms-powered-long-audio-logging-with","repo_url":"https://github.com/jishengbai/audiolog","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"acoustic-scene-classification","task_name":"Acoustic Scene Classification"},{"task_slug":"audio-captioning","task_name":"Audio captioning"},{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"event-detection","task_name":"Event Detection"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"scene-classification","task_name":"Scene Classification"},{"task_slug":"sound-event-detection","task_name":"Sound Event Detection"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.12371","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}