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| Language: | English |
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2026
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| Online Access: | https://doi.org/10.5281/zenodo.20073299 |
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| author | Yalçınkaya, Mehmet Ali |
| author_facet | Yalçınkaya, Mehmet Ali |
| contents | <p>-Updated to new version with corrected co-occurrence interpretation, OOF protocol, and 500 m external validation<br><br>This repository contains the complete source code for the end-to-end urban noise <br>monitoring pipeline described in the manuscript "An End-to-End Deep Learning Pipeline <br>for Urban Noise Monitoring with Spatiotemporal Validation" (under review at Scientific Reports).</p> <p>The pipeline extends from raw data collection (SONYC-UST v2.3 audio + NYC 311 complaint records) <br>through CNN14-based multi-label classification, calibration analysis, TorchScript deployment, <br>spatiotemporal mapping, and external corroboration analysis.</p> <ul> <li>Contents <ul> <li>15 Jupyter notebooks, including the manuscript-reproduction workflow and one legacy data-acquisition notebook retained for transparency</li> <li>README.md with detailed usage instructions, hardware requirements, and reproducibility settings</li> <li>requirements.txt with fully pinned dependency versions</li> </ul> </li> <li>Hardware Tested<br>NVIDIA GeForce RTX 3060 (12 GB VRAM), Python 3.11, PyTorch 2.5.1 with CUDA 12.1.</li> <li>Dataset Access <ul> <li>SONYC-UST v2.3: https://zenodo.org/record/3966543</li> <li>NYC 311 Noise Complaints: NYC Open Data Portal (dataset 76ig-c548)</li> </ul> </li> <li>Citation<br>Please refer to the README.md for citation details. The full manuscript citation <br>will be updated upon publication.</li> <li>License<br>MIT License</li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20073299 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | An End-to-End Deep Learning Pipeline for Urban Noise Monitoring with Spatiotemporal Validation Yalçınkaya, Mehmet Ali urban noise monitoring audio classification deep learning CNN14 SONYC-UST multi-label classification spatiotemporal analysis probability calibration TorchScript environmental sound <p>-Updated to new version with corrected co-occurrence interpretation, OOF protocol, and 500 m external validation<br><br>This repository contains the complete source code for the end-to-end urban noise <br>monitoring pipeline described in the manuscript "An End-to-End Deep Learning Pipeline <br>for Urban Noise Monitoring with Spatiotemporal Validation" (under review at Scientific Reports).</p> <p>The pipeline extends from raw data collection (SONYC-UST v2.3 audio + NYC 311 complaint records) <br>through CNN14-based multi-label classification, calibration analysis, TorchScript deployment, <br>spatiotemporal mapping, and external corroboration analysis.</p> <ul> <li>Contents <ul> <li>15 Jupyter notebooks, including the manuscript-reproduction workflow and one legacy data-acquisition notebook retained for transparency</li> <li>README.md with detailed usage instructions, hardware requirements, and reproducibility settings</li> <li>requirements.txt with fully pinned dependency versions</li> </ul> </li> <li>Hardware Tested<br>NVIDIA GeForce RTX 3060 (12 GB VRAM), Python 3.11, PyTorch 2.5.1 with CUDA 12.1.</li> <li>Dataset Access <ul> <li>SONYC-UST v2.3: https://zenodo.org/record/3966543</li> <li>NYC 311 Noise Complaints: NYC Open Data Portal (dataset 76ig-c548)</li> </ul> </li> <li>Citation<br>Please refer to the README.md for citation details. The full manuscript citation <br>will be updated upon publication.</li> <li>License<br>MIT License</li> </ul> |
| title | An End-to-End Deep Learning Pipeline for Urban Noise Monitoring with Spatiotemporal Validation |
| topic | urban noise monitoring audio classification deep learning CNN14 SONYC-UST multi-label classification spatiotemporal analysis probability calibration TorchScript environmental sound |
| url | https://doi.org/10.5281/zenodo.20073299 |