Logbook: Extremely Long-form Audio Event Understanding
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 mitigate...
| Authors | Kwanghee Choi, Suwon Shon, Dmitriy Serdyuk et al. |
|---|---|
| Primary category | eess.AS |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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