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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Book ChapterDOI
Opportunities and Risks of Delegating Sensing Tasks to the Crowd
Delphine Reinhardt,Frank Dürr +1 more
TL;DR: An overview of existing applications is provided and both the opportunities and risks raised by the contributions of volunteers to the sensing process are detailed.
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Zhiwen Yu,Zhu Wang +1 more
TL;DR: This chapter presents the mobile device-enabled behavior recognition approach, which is a typical type of sensor-based behavior recognition, followed by a discussion on the key issues of developing behavior recognition systems using mobile devices.
Journal ArticleDOI
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Christian Webersik,Jose J. Gonzalez,Julie Dugdale,Bjørn Erik Munkvold,Ole-Christoffer Granmo +4 more
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Social Web for Large-Scale Biosensors
TL;DR: This paper will present as well an application scenario for such predictions, namely fetus health monitoring in pregnant woman, presenting a new non-invasive portable alternative system that allows long-term pregnancy surveillance.
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