A citizen science approach to support joint air quality and noise monitoring in urban areas

Stamatiadou ME, Vryzas N, Vrysis L, Saridou T, Dimoulas C. A citizen science approach to support joint air quality and noise monitoring in urban areas. In: AES Convention 152. Audio Engineering Society; 2022. Paper 10591. Available from: https://aes.org/publications/elibrary-page/?id=21704

@inproceedings{Stamatiadou2022_21704,
  author = {Stamatiadou, Marina Eirini and Vryzas, Nikolaos and Vrysis, Lazaros and Saridou, Theodora and Dimoulas, Charalampos},
  title = {{A citizen science approach to support joint air quality and noise monitoring in urban areas}},
  booktitle = {AES Convention 152},
  note = {Paper 10591},
  year = {2022},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=21704}
}

TY  - CPAPER
TI  - A citizen science approach to support joint air quality and noise monitoring in urban areas
AU  - Stamatiadou, Marina Eirini
AU  - Vryzas, Nikolaos
AU  - Vrysis, Lazaros
AU  - Saridou, Theodora
AU  - Dimoulas, Charalampos
T2  - AES Convention 152
M1  - Paper 10591
PY  - 2022
DA  - 2022/05/06
UR  - https://aes.org/publications/elibrary-page/?id=21704
PB  - Audio Engineering Society
LA  - en
AB  - In the present work, a crowdsourcing approach is designed, to investigate the correlation between air and noise pollution in urban areas. Citizens are requested to provide air quality measurements and audio recordings using a prototype mobile application specially designed to motivate them to undertake the task of audiovisual capturing. Different use case scenarios of the application are presented, along with the technical specifications and service-based architecture. The UrESC22 dataset is formed, a subset of the ESC50 benchmark dataset consisting of all classes related to polluting activities (vehicles, engines, etc.). The dataset is used to train a convolutional neural network classifier for the detection of audio events related to air pollution.
ER  -