research-article

Audio Analysis as a Control Knob for Social Sensing

Authors:

Graham BentAuthors Info & Claims

SocialSens'17: Proceedings of the 2nd International Workshop on Social Sensing

Pages 75 - 80

https://doi.org/10.1145/3055601.3055616

Published: 18 April 2017 Publication History

Get Access

Abstract

While humans can act as effective sensors, human input is subject to a high degree of error and highly dependent on the context. Furthermore, extracting the signal from the noise for social sensing is a difficult challenge. One approach to improving the accuracy of social sensing is to use physical sensors as a control knob for social sensing algorithms. In this paper, we present an architecture for using audio sensors as a way to control an algorithm used for social sensing of interesting events. We present various use cases where the architecture is applicable, and go into the details of one specific use case, namely using crowd behavior in a golf-course to identify and control social media feeds related to the course.

References

[1]

2016. DCASE 2016 Challenge. (2016). http://www.cs.tut.fi/sgn/arg/dcase2016/challenge

Google Scholar

[2]

Maged N Kamel Boulos, Bernd Resch, David N Crowley, John G Breslin, Gunho Sohn, Russ Burtner, William A Pike, Eduardo Jezierski, and Kuo-Yu Slayer Chuang. 2011. Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples. International Journal of Health Geographics 10, 1 (2011), 67.

Crossref

Google Scholar

[3]

Bernd Girod, Vijay Chandrasekhar, David M Chen, Ngai-Man Cheung, Radek Grzeszczuk, Yuriy Reznik, Gabriel Takacs, Sam S Tsai, and Ramakrishna Vedantham. 2011. Mobile visual search. IEEE signal processing magazine 28, 4 (2011), 61--76.

Crossref

Google Scholar

[4]

Wei Han, Cheong-Fat Chan, Chiu-Sing Choy, and Kong-Pang Pun. 2006. An efficient MFCC extraction method in speech recognition. In Proc. of IEEE International Symposium on Circuits and Systems, 2006. IEEE.

Google Scholar

[5]

Hong Lu, Wei Pan, Nicholas D Lane, Tanzeem Choudhury, and Andrew T Campbell. 2009. SoundSense: scalable sound sensing for people-centric applications on mobile phones. In Proceedings of the 7th international conference on Mobile systems, applications, and services. ACM, 165--178.

Digital Library

Google Scholar

[6]

Anmol Madan, Manuel Cebrian, Sai Moturu, Katayoun Farrahi, and others. 2012. Sensing the health state of a community. IEEE Pervasive Computing 11, 4 (2012), 36--45.

Digital Library

Google Scholar

[7]

Min Mun, Sasank Reddy, Katie Shilton, Nathan Yau, Jeff Burke, Deborah Estrin, Mark Hansen, Eric Howard, Ruth West, and Péter Boda. 2009. PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. In Proceedings of the 7th international conference on Mobile systems, applications, and services. ACM, 55--68.

Digital Library

Google Scholar

[8]

Rohan Narayana Murty, Geoffrey Mainland, Ian Rose, Atanu Roy Chowdhury, Abhimanyu Gosain, Josh Bers, and Matt Welsh. 2008. Citysense: An urban-scale wireless sensor network and testbed. In 2008 IEEE Conference on Technologies for Homeland Security. IEEE, 583--588.

Crossref

Google Scholar

[9]

Alun Preece, Colin Roberts, David Rogers, William Webberley, Martin Innes, and Dave Braines. 2016. From open source communications to knowledge. In SPIE Defense + Security. International Society for Optics and Photonics.

Google Scholar

[10]

Mervyn Stone. 1974. Cross-validatory choice and assessment of statistical predictions. Journal of the royal statistical society. Series B (Methodological) (1974), 111--147.

Google Scholar

[11]

Shiqiang Wang. 2015. Dynamic service placement in mobile micro-clouds. Ph.D. Dissertation. Imperial College London.

Google Scholar

Cited By

View all

Grueneberg KKo BWood DWang XSteuer DLim Y(2019)IoT Data Management System for Rapid Development of Machine Learning Models2019 IEEE International Conference on Cognitive Computing (ICCC)10.1109/ICCC.2019.00021(59-63)Online publication date: Jul-2019
https://doi.org/10.1109/ICCC.2019.00021

Recommendations

Image-based social sensing: combining AI and the crowd to mine policy-adherence indicators from Twitter
ICSE-SEIS '21: Proceedings of the 43rd International Conference on Software Engineering: Software Engineering in Society

Social Media provides a trove of information that, if aggregated and analysed appropriately can provide important statistical indicators to policy makers. In some situations these indicators are not available through other mechanisms. For example, given ...
Social Sensing: Assessing Social Functioning of Patients Living with Schizophrenia using Mobile Phone Sensing
CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Impaired social functioning is a symptom of mental illness (e.g., depression, schizophrenia) and a wide range of other conditions (e.g., cognitive decline in the elderly, dementia). Today, assessing social functioning relies on subjective evaluations and ...
The potential of wearable technology for monitoring social interactions based on interpersonal synchrony
WearSys '18: Proceedings of the 4th ACM Workshop on Wearable Systems and Applications

Sensing data from wearables have been extensively evaluated for fitness tracking, health monitoring or rehabilitation of individuals. However, we believe that wearable sensing can go beyond the individual and offer insights into social dynamics and ...

Comments

Information & Contributors

Information

Published In

SocialSens'17: Proceedings of the 2nd International Workshop on Social Sensing

April 2017

97 pages

ISBN:9781450349772

DOI:10.1145/3055601

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 April 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Army Research Laboratory

Conference

CPS Week '17

Sponsor:

SIGBED

CPS Week '17: Cyber Physical Systems Week 2017

April 18 - 21, 2017

PA, Pittsburgh, USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
119
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)1

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Grueneberg KKo BWood DWang XSteuer DLim Y(2019)IoT Data Management System for Rapid Development of Machine Learning Models2019 IEEE International Conference on Cognitive Computing (ICCC)10.1109/ICCC.2019.00021(59-63)Online publication date: Jul-2019
https://doi.org/10.1109/ICCC.2019.00021

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Recommendations

Image-based social sensing: combining AI and the crowd to mine policy-adherence indicators from Twitter

Social Sensing: Assessing Social Functioning of Patients Living with Schizophrenia using Mobile Phone Sensing

The potential of wearable technology for monitoring social interactions based on interpersonal synchrony

Comments

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Recommendations

Image-based social sensing: combining AI and the crowd to mine policy-adherence indicators from Twitter

Social Sensing: Assessing Social Functioning of Patients Living with Schizophrenia using Mobile Phone Sensing

The potential of wearable technology for monitoring social interactions based on interpersonal synchrony

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations