Logbook: Extremely Long-form Audio Event Understanding
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Electrical Engineering and Systems Science > Audio and Speech Processing
arXiv:2610.07338 (eess)
[Submitted on 5 Oct 2026]
Title:Logbook: Extremely Long-form Audio Event Understanding
Authors:Kwanghee Choi, Suwon Shon, Dmitriy Serdyuk, Guitang Lan, Chao-Wei Huang, Mohammad Sadegh Rasooli, Sangeeta Srivastava, Zhaojiang Lin, Saurabh Adya, Ming Sun
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Abstract:Audio benchmarks are built around short, pre-segmented clips, limiting model design to brief inputs or fixed vocabularies. To close this gap, we introduce Logbook, a benchmark for hour-scale audio understanding, with recordings ranging from ten minutes to six days. Given a continuous audio recording and an event label vocabulary, a system must predict a gap-free segmentation with an event label and a description per segment. We compare 52 systems, end-to-end and cascaded, and ablate fine-tuning, context length, and reasoning budget. We find the task tractable, though the best systems remain below the human reference. Also, over-segmentation is pervasive, and fine-tuning partially mitigates it. Finally, end-to-end are often better than cascaded systems, but degrades with longer context.
| Comments: | Submitted to ICASSP 2027. Source code available at this https URL |
| Subjects: | Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD) |
| Cite as: | arXiv:2610.07338 [eess.AS] |
| (or arXiv:2610.07338v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07338
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Logbook: Extremely Long-form Audio Event Understanding, by Kwanghee Choi and 9 other authors
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