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:
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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Journal ArticleDOI
Probabilistic Mining of Socio-Geographic Routines From Mobile Phone Data
TL;DR: This paper uses an unsupervised approach, based on probabilistic topic models, to discover latent human activities in terms of the joint interaction and location behaviors of 97 individuals over the course of approximately a 10-month period using data from MIT's Reality Mining project.
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Comprehensive Context Recognizer Based on Multimodal Sensors in a Smartphone
TL;DR: This work introduces a comprehensive approach for context aware applications that utilizes the multimodal sensors in smartphones and presents a novel feature selection algorithm for the accelerometer classification module.
Proceedings ArticleDOI
Probabilistic registration for large-scale mobile participatory sensing
TL;DR: A probabilistic registration approach is presented, based on a realistic human mobility model, that allows devices to decide whether or not to register their sensing services depending on the probability of other, equivalent devices being present at the locations of their expected path.
Proceedings ArticleDOI
SemaDroid: A Privacy-Aware Sensor Management Framework for Smartphones
Zhi Xu,Sencun Zhu +1 more
TL;DR: SemaDroid is proposed, which extends the existing sensor management framework on Android to provide comprehensive and fine-grained access control over onboard sensors and supports context-aware and quality-of-sensing based access control policies.
Proceedings ArticleDOI
Reflex: using low-power processors in smartphones without knowing them
TL;DR: Using smartphone sensing applications reported in recent literature, Reflex supports a programming style very close to contemporary smartphone programming, and greatly reduces efforts in programming heterogeneous smartphones, eliminating up to 38% of the source lines of application code.
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