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
Individual Behavior Recognition
Zhiwen Yu,Zhu Wang +1 more
TL;DR: This chapter presents some of the recent research advances on individual behavior sensing and recognition by leveraging GPS trajectories and discusses how to recognize human behaviors by using smartphones.
DissertationDOI
A framework for mobile activity recognition
TL;DR: A hybrid method that integrates Latent Dirichlet Allocation with conventional classifiers for learning a generic activity model with minimum annotated data is proposed and a framework for low-level activity recognition with dynamically available sensors is proposed.
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TL;DR: This paper argues that RDS can be employed to enable a broad range of new applications and enhance existing ones and discusses a number of applications that can be enabled or enhanced by RDS.
Proceedings ArticleDOI
A Compact Size and Low Profile Rectangular Slot Monopole Antenna for UWB Body Centric Applications
Isah Musa Danjuma,Mobayode O. Akinsolu,Buhari Mohammad,Eya Eya,Raed AbdnAlhameed,J. M. Noras,Bo Liu +6 more
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Proceedings ArticleDOI
Robust voice activity detection for social sensing
Sebastian Feese,Gerhard Tröster +1 more
TL;DR: It is shown that speech activity of firefighters can be detected with 85% accuracy when using a smartphone that was placed in the firefighting jacket and even in low signal-to-noise conditions with up to 92% accuracy.
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