What makes New York so noisy? Reasoning noise pollution by mining multimodal geo-social big data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Noise pollution in modern cities is getting worse and sound sensors are sparse and costly, but it is highly demanded to have a system that can help reason and present the noise pol-lution at any region in urban areas. In this work, we leverage multimodal geo-social media data on Foursquare, Twitter, Flickr, and Gowalla in New York City, to infer and visualize the volume and the composition of noise pollution for ev-ery region in NYC. Using NYC 311 noise complaint records as the approximation of noise pollution for validation, we develop a joint inference and visualization system that inte-grates multimodal features, including geographical, mobil-ity, visual, and social, with a graph-based learning model to infer the noise compositions of regions. Experimental re-sults show that our model can achieve promising results with substantially few training data, compared to state-of-The-Art methods. A NYC Urban Noise Diagnotor system is devel-oped and allowed users to understand the noise composition of any region of NYC in an interactive manner.

Original languageEnglish
Title of host publicationMM 2015 - Proceedings of the 2015 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Pages181-184
Number of pages4
ISBN (Electronic)9781450334594
DOIs
Publication statusPublished - 2015 Oct 13
Event23rd ACM International Conference on Multimedia, MM 2015 - Brisbane, Australia
Duration: 2015 Oct 262015 Oct 30

Publication series

NameMM 2015 - Proceedings of the 2015 ACM Multimedia Conference

Other

Other23rd ACM International Conference on Multimedia, MM 2015
CountryAustralia
CityBrisbane
Period15-10-2615-10-30

Fingerprint

Noise pollution
Chemical analysis
Acoustic noise
Visualization
Acoustic waves
Sensors
Big data

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Hsieh, H-P., Yen, T. C., & Li, C-T. (2015). What makes New York so noisy? Reasoning noise pollution by mining multimodal geo-social big data. In MM 2015 - Proceedings of the 2015 ACM Multimedia Conference (pp. 181-184). (MM 2015 - Proceedings of the 2015 ACM Multimedia Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/2733373.2809931
Hsieh, Hsun-Ping ; Yen, Tzu Chi ; Li, Cheng-Te. / What makes New York so noisy? Reasoning noise pollution by mining multimodal geo-social big data. MM 2015 - Proceedings of the 2015 ACM Multimedia Conference. Association for Computing Machinery, Inc, 2015. pp. 181-184 (MM 2015 - Proceedings of the 2015 ACM Multimedia Conference).
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Hsieh, H-P, Yen, TC & Li, C-T 2015, What makes New York so noisy? Reasoning noise pollution by mining multimodal geo-social big data. in MM 2015 - Proceedings of the 2015 ACM Multimedia Conference. MM 2015 - Proceedings of the 2015 ACM Multimedia Conference, Association for Computing Machinery, Inc, pp. 181-184, 23rd ACM International Conference on Multimedia, MM 2015, Brisbane, Australia, 15-10-26. https://doi.org/10.1145/2733373.2809931

What makes New York so noisy? Reasoning noise pollution by mining multimodal geo-social big data. / Hsieh, Hsun-Ping; Yen, Tzu Chi; Li, Cheng-Te.

MM 2015 - Proceedings of the 2015 ACM Multimedia Conference. Association for Computing Machinery, Inc, 2015. p. 181-184 (MM 2015 - Proceedings of the 2015 ACM Multimedia Conference).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Hsieh H-P, Yen TC, Li C-T. What makes New York so noisy? Reasoning noise pollution by mining multimodal geo-social big data. In MM 2015 - Proceedings of the 2015 ACM Multimedia Conference. Association for Computing Machinery, Inc. 2015. p. 181-184. (MM 2015 - Proceedings of the 2015 ACM Multimedia Conference). https://doi.org/10.1145/2733373.2809931