Geo-ATBench: A Benchmark for Geospatial Audio Tagging with Geospatial Semantic Context
Environmental sound understanding in computational auditory scene analysis (CASA) is often formulated as an audio-only recognition problem. This formulation leaves a persistent drawback in multi-label audio tagging (AT): acoustic similarity can make certain events difficult to separate from waveforms alone. In such cases, disambiguating cues often lie outside the waveform. Geospatial semantic context (GSC), derived from geographic information system data, e.g., points of interest (POI), provides location-tied environmental priors that can help reduce this ambiguity. A systematic study of this direction is enabled through the proposed geospatial audio tagging (Geo-AT) task, which condition...
| Authors | Yuanbo Hou, Yanru Wu, Qiaoqiao Ren et al. |
|---|---|
| Primary category | eess.AS |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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