Data · dataset · 2026
NoisySoundBD: A Geotagged Noise Audio Dataset from Dhaka, Bangladesh for Noise-Source Classification
Listed in Teesside University Research Data Repository
NoisySoundBD is a geotagged and annotated urban noise dataset designed for noise-source classification.
Description
It contains 777 short environmental-audio clips collected from real-world urban settings, each paired with structured metadata such as location, timestamp, decibel statistics, recording context, severity, and human-reviewed labels. The dataset is organized into three broad classes: construction_industry, misc, and traffic.
It can support research in urban noise monitoring, environmental audio classification, geospatial noise analysis, and low-resource audio machine learning. For more details and benchmarking performance, please read our research paper (link is provided).
Links
Where it is published
- DOI doi.org/10.17632/y35mpdn2jt.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Computer Science & AI · Deep learning · Earth & Environmental Science · Machine learning · Social Science · Urban and regional planning
- Inferred from text
- Audio 75%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/y35mpdn2jt.1 | 6 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:field:461103 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Deep Learning'] |
| concepts[field].anzsrc:group:3304 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Urban Planning'] |
| concepts[field].anzsrc:group:4611 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[modality].local:modality:audio | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |