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
A probabilistic approach to socio-geographic reality mining
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Journal ArticleDOI
Audio-Based Activities of Daily Living (ADL) Recognition with Large-Scale Acoustic Embeddings from Online Videos
Dawei Liang,Edison Thomaz +1 more
TL;DR: In this paper, a framework for audio-based activity recognition that makes use of millions of embedding features from public online video sound clips is proposed. But, it does not require further feature processing or outliers filtering as in prior work.
Proceedings ArticleDOI
Activity recognition on handheld devices for pedestrian indoor navigation
TL;DR: An inertial sensor-based approach to activity recognition for pedestrian indoor navigation that achieves a classification accuracy of 91% for new users and offers a 30% improvement compared to state-of-the-art approaches is proposed.
Patent
Using out-band information to improve wireless communications
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Proceedings ArticleDOI
CrowdWatch: pedestrian safety assistance with mobile crowd sensing
TL;DR: CrowdWatch, a system that leverages crowd-powered smartphone sensing to make fine-grained characterization of the sensing field and provides context-aware alerts to pedestrians for their safety, is proposed.
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