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arXiv · 2610.07338

Logbook: Extremely Long-form Audio Event Understanding

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.

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Kwanghee Choi, Suwon Shon, Dmitriy Serdyuk, Guitang Lan, Chao-Wei Huang, Mohammad Sadegh Rasooli, Sangeeta Srivastava, Zhaojiang Lin, Saurabh Adya, Ming Sun. 2026-10-05. Logbook: Extremely Long-form Audio Event Understanding. https://arxiv.org/abs/2610.07338

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