ASPED: An Audio Dataset for Detecting Pedestrians
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917569101824000 |
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| author | Seshadri, Pavan Han, Chaeyeon Koo, Bon-Woo Posner, Noah Guhathakurta, Subhrajit Lerch, Alexander |
| author_facet | Seshadri, Pavan Han, Chaeyeon Koo, Bon-Woo Posner, Noah Guhathakurta, Subhrajit Lerch, Alexander |
| contents | We introduce the new audio analysis task of pedestrian detection and present a new large-scale dataset for this task. While the preliminary results prove the viability of using audio approaches for pedestrian detection, they also show that this challenging task cannot be easily solved with standard approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_06531 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ASPED: An Audio Dataset for Detecting Pedestrians Seshadri, Pavan Han, Chaeyeon Koo, Bon-Woo Posner, Noah Guhathakurta, Subhrajit Lerch, Alexander Audio and Speech Processing Sound We introduce the new audio analysis task of pedestrian detection and present a new large-scale dataset for this task. While the preliminary results prove the viability of using audio approaches for pedestrian detection, they also show that this challenging task cannot be easily solved with standard approaches. |
| title | ASPED: An Audio Dataset for Detecting Pedestrians |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2309.06531 |