TY - JOUR
T1 - Low-cost distributed acoustic sensor network for real-time urban sound monitoring
AU - Vidaña-Vila, Ester
AU - Navarro, Joan
AU - Borda-Fortuny, Cristina
AU - Stowell, Dan
AU - Alsina-Pagès, Rosa Ma
N1 - Funding Information:
Funding: This research was partially funded by the Secretaria d’Universitats i Recerca of the Department of Business and Knowledge of the Generalitat de Catalunya under grants 2017-SGR-966 and 2017-SGR-977. Ester Vidaña-Vila and Rosa Ma Alsina-Pagès would like to thank La Salle Campus BCN - URL for partially funding the joint research with Queen Mary University (London) in the framework of Ms Vidaña-Vila PhD Thesis. Also, this work was partially funded by the Spanish Ministry of Science, Innovation and University, the Investigation State Agency and the European Regional Development Fund (ERDF) under grant RTI2018-097066-B-I00 for Joan Navarro and Cristina Borda-Fortuny.
Funding Information:
Acknowledgments: The authors would like to thank Lisa Kinnear for her never-ending patience, support and thorough review of this work. Also, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.
Funding Information:
This research was partially funded by the Secretaria d?Universitats i Recerca of the Department of Business and Knowledge of the Generalitat de Catalunya under grants 2017-SGR-966 and 2017-SGR-977. Ester Vida?a-Vila and Rosa Ma Alsina-Pag?s would like to thank La Salle Campus BCN-URL for partially funding the joint research with Queen Mary University (London) in the framework of Ms Vida?a-Vila PhD Thesis. Also, this work was partially funded by the Spanish Ministry of Science, Innovation and University, the Investigation State Agency and the European Regional Development Fund (ERDF) under grant RTI2018-097066-B-I00 for Joan Navarro and Cristina Borda-Fortuny. Acknowledgments: The authors would like to thank Lisa Kinnear for her never-ending patience, support and thorough review of this work. Also, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/12
Y1 - 2020/12
N2 - Continuous exposure to urban noise has been found to be one of the major threats to citizens’ health. In this regard, several organizations are devoting huge efforts to designing new in-field systems to identify the acoustic sources of these threats to protect those citizens at risk. Typically, these prototype systems are composed of expensive components that limit their large-scale deployment and thus reduce the scope of their measurements. This paper aims to present a highly scalable low-cost distributed infrastructure that features a ubiquitous acoustic sensor network to monitor urban sounds. It takes advantage of (1) low-cost microphones deployed in a redundant topology to improve their individual performance when identifying the sound source, (2) a deep-learning algorithm for sound recognition, (3) a distributed data-processing middleware to reach consensus on the sound identification, and (4) a custom planar antenna with an almost isotropic radiation pattern for the proper node communication. This enables practitioners to acoustically populate urban spaces and provide a reliable view of noises occurring in real time. The city of Barcelona (Spain) and the UrbanSound8K dataset have been selected to analytically validate the proposed approach. Results obtained in laboratory tests endorse the feasibility of this proposal.
AB - Continuous exposure to urban noise has been found to be one of the major threats to citizens’ health. In this regard, several organizations are devoting huge efforts to designing new in-field systems to identify the acoustic sources of these threats to protect those citizens at risk. Typically, these prototype systems are composed of expensive components that limit their large-scale deployment and thus reduce the scope of their measurements. This paper aims to present a highly scalable low-cost distributed infrastructure that features a ubiquitous acoustic sensor network to monitor urban sounds. It takes advantage of (1) low-cost microphones deployed in a redundant topology to improve their individual performance when identifying the sound source, (2) a deep-learning algorithm for sound recognition, (3) a distributed data-processing middleware to reach consensus on the sound identification, and (4) a custom planar antenna with an almost isotropic radiation pattern for the proper node communication. This enables practitioners to acoustically populate urban spaces and provide a reliable view of noises occurring in real time. The city of Barcelona (Spain) and the UrbanSound8K dataset have been selected to analytically validate the proposed approach. Results obtained in laboratory tests endorse the feasibility of this proposal.
KW - Acoustic event detection
KW - Acoustic propagation
KW - Distributed consensus
KW - Isotropic radiation pattern planar antenna
KW - Noise management
KW - Signal processing
KW - Ubiquitous acoustic sensor network
UR - https://www.scopus.com/pages/publications/85097774450
UR - https://hdl.handle.net/20.500.14342/3499
U2 - 10.3390/electronics9122119
DO - 10.3390/electronics9122119
M3 - Article
AN - SCOPUS:85097774450
SN - 2079-9292
VL - 9
SP - 1
EP - 25
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 12
M1 - 2119
ER -