STeLiN-US Database

Download Link

STeLiN-US Database
The dataset is 4.0 GB in size and can be downloaded in a zip file from the above link.

Introduction

The Spatio-temporally Linked Neighborhood Urban Sound (STeLiN-US) database is semi-synthesized; that is, each sample is generated by leveraging diverse sets of real urban sounds with crawled information of real-world user behaviors over time.

Dataset Specification:

STeLiN-US dataset is consisting of 5 minutes audio segments representing 5 acoustic scenes or microphone locations:

1. Street
2. Metro-Station
3. Park
4. School-Playground
5. Café

Audio segment at each scene is synthesized for 15 discrete hours of the day from 7am to 9pm, equally distributed for each day of the week from Monday to Sunday. For 5 locations on 7 days with 15 discrete timestamps representing each audio segment accumulate to 525 total audio segments representing 43 hours 45 minutes of duration. We use 14 acoustic sound classes divided into event and background as below:

Events	Backgrounds
Vehicle, Children Playing, Street Music, Phone Ring, School Bell, Car Horn, Bird, and Dog Bark	Train, Pedestrian, Cafe Crowd, Urban Park, River, and Fountain

Citing

S. Chunarkar, B. Su, C. Lee, "STELIN-US: A Spatio-Temporally Linked Neighborhood Urban Sound Database," in Proceedings of the 8th Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), 2023, pp. 21–25.

@inproceedings{Chunarkar2023,
    author = "Chunarkar, Snehit and Su, Bo-Hao and Lee, Chi-Chun",
    title = "{STELIN-US}: A Spatio-Temporally Linked Neighborhood Urban Sound Database",
    booktitle = "Proceedings of the 8th Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023)",
    address = "Tampere, Finland",
    month = "September",
    year = "2023",
    pages = "21--25"
    }

File Structure on Github

STeLiN-US
|
|____Dataset
|    |____Image
|         |____Map_with_Graph.png
|    |____README.md
|
|____SED
|    |____Images
|         |____Model_Git.jpg
|         |____Train_Valid_loss.png
|         |____classification_report.jpg
|         |____epoch_30_results.jpg
|         |____metric.png
|    |____Model.py
|    |____README.md
|    |____STeLiN_US_Data.py
|    |____df_polyphonic_meta.pkl
|    |____df_polyphonic_meta.py
|    |____main.py
|
|____README.md