Proceedings ArticleDOI
SoundSense: scalable sound sensing for people-centric applications on mobile phones
Hong Lu,Wei Pan,Nicholas D. Lane,Tanzeem Choudhury,Andrew T. Campbell +4 more
- pp 165-178
TLDR
This paper proposes SoundSense, a scalable framework for modeling sound events on mobile phones that represents the first general purpose sound sensing system specifically designed to work on resource limited phones and demonstrates that SoundSense is capable of recognizing meaningful sound events that occur in users' everyday lives.Abstract:Â
Top end mobile phones include a number of specialized (e.g., accelerometer, compass, GPS) and general purpose sensors (e.g., microphone, camera) that enable new people-centric sensing applications. Perhaps the most ubiquitous and unexploited sensor on mobile phones is the microphone - a powerful sensor that is capable of making sophisticated inferences about human activity, location, and social events from sound. In this paper, we exploit this untapped sensor not in the context of human communications but as an enabler of new sensing applications. We propose SoundSense, a scalable framework for modeling sound events on mobile phones. SoundSense is implemented on the Apple iPhone and represents the first general purpose sound sensing system specifically designed to work on resource limited phones. The architecture and algorithms are designed for scalability and Soundsense uses a combination of supervised and unsupervised learning techniques to classify both general sound types (e.g., music, voice) and discover novel sound events specific to individual users. The system runs solely on the mobile phone with no back-end interactions. Through implementation and evaluation of two proof of concept people-centric sensing applications, we demostrate that SoundSense is capable of recognizing meaningful sound events that occur in users' everyday lives.read more
Citations
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Proceedings ArticleDOI
PriSense: Privacy-Preserving Data Aggregation in People-Centric Urban Sensing Systems
TL;DR: PriSense is a novel solution to privacy-preserving data aggregation in people- centric urban sensing systems and can support a wide range of statistical additive and non-additive aggregation functions such as Sum, Average, Variance, Count, Max/Min, Median, Histogram, and Percentile with accurate aggregation results.
Proceedings ArticleDOI
iSleep: unobtrusive sleep quality monitoring using smartphones
Tian Hao,Guoliang Xing,Gang Zhou +2 more
TL;DR: iSleep uses the built-in microphone of the smartphone to detect the events that are closely related to sleep quality, including body movement, couch and snore, and infers quantitative measures of sleep quality using off-the-shelf smartphone.
Proceedings ArticleDOI
Wolverine: Traffic and road condition estimation using smartphone sensors
TL;DR: This work extends a prior study to improve the algorithm based on using accelerometer, GPS and magnetometer sensor readings for traffic and road conditions detection and proposes Wolverine - a non-intrusive method that uses sensors present on smartphones.
Proceedings ArticleDOI
Pay as How Well You Do: A Quality Based Incentive Mechanism for Crowdsensing
Dan Peng,Fan Wu,Guihai Chen +2 more
TL;DR: This paper proposes to pay the participants as how well they do, to motivate the rational participants to perform data sensing efficiently, and estimates the quality of sensing data, and offers each participant a reward based on her effective contribution.
Proceedings ArticleDOI
Diagnosing New York city's noises with ubiquitous data
TL;DR: This paper infer the fine-grained noise situation (consisting of a noise pollution indicator and the composition of noises) of different times of day for each region of NYC, by using the 311 complaint data together with social media, road network data, and Points of Interests (POIs).
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