Mobiusi/School-Area-Noise-Pollution-Assessment-Dataset
School Area Noise Pollution Assessment Dataset The core advantage of this dataset lies in its high level of data quality assurance, with annotation accuracy exceeding 95%. The standardization of the recording environment and strict quality control measures ensure data consistency and completeness. Technological innovations include the use of advanced sound source localization technology and automatic speech recognition methods, which enhance the efficiency and accuracy of noise… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/School-Area-Noise-Pollution-Assessment-Dataset.
School Area Noise Pollution Assessment Dataset
The core advantage of this dataset lies in its high level of data quality assurance, with annotation accuracy exceeding 95%. The standardization of the recording environment and strict quality control measures ensure data consistency and completeness. Technological innovations include the use of advanced sound source localization technology and automatic speech recognition methods, which enhance the efficiency and accuracy of noise event annotation. In terms of application value, this dataset supports the development of more efficient noise monitoring and management solutions, enabling real-time identification of noise sources within scenes. Compared to existing datasets, it offers a longer time span and wider geographical coverage. The unique samples of low noise and mixed environments of complex sound sources in the dataset provide a unique perspective and resources for research and development. Additionally, the dataset is highly scalable and can be adapted to different environmental monitoring tasks by adding collection devices and expanding the data scope.
Technical Specifications
Compliance Statement
<table> <tr> <td>Authorization Type</td> <td>CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)</td> </tr> <tr> <td>Commercial Use</td> <td>Requires exclusive subscription or authorization contract (monthly or per-invocation charging)</td> </tr> <tr> <td>Privacy and Anonymization</td> <td>No PII, no real company names, simulated scenarios follow industry standards</td> </tr> <tr> <td>Compliance System</td> <td>Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs</td> </tr> </table>
Source & Contact
If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com
