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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Proceedings ArticleDOI
ACE: exploiting correlation for energy-efficient and continuous context sensing
TL;DR: The ACE can reduce sensing costs of three context-aware applications by about 4.2 times, compared to a raw sensor data cache shared across applications, with a very small memory and processing overhead.
Journal ArticleDOI
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Veljko Pejovic,Mirco Musolesi +1 more
TL;DR: A survey of phenomena that mobile phones can infer and predict, and a description of machine learning techniques used for such predictions are presented, paving the way for full-fledged anticipatory mobile computing.
Journal ArticleDOI
Sensing as a Service: Challenges, Solutions and Future Directions
TL;DR: In this article, the authors introduce a new concept, sensing as a service (S2aaS), i.e., providing sensing services using mobile phones via a cloud computing system, which can enable attractive sensing applications in different domains, such as environmental monitoring, social networking, healthcare, transportation, etc.
Proceedings ArticleDOI
IODetector: a generic service for indoor outdoor detection
TL;DR: This paper prototype the IODetector on Android mobile phones and evaluate the system comprehensively with data collected from 19 traces which include 84 different places during one month period, employing different phone models.
Book ChapterDOI
SpeakerSense: energy efficient unobtrusive speaker identification on mobile phones
TL;DR: SpeakerSense is built, a speaker identification prototype that uses a heterogeneous multi-processor hardware architecture that splits computation between a low power processor and the phone's application processor to enable continuous background sensing with minimal power requirements.
References
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Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Pattern Recognition and Machine Learning
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