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 Article
Localising speech, footsteps and other sounds using resource-constrained devices
Yukang Guo,Mike Hazas +1 more
TL;DR: This paper identifies methods for resource-constrained devices in a sensor network to detect, classify and locate acoustic events such as speech, footsteps and objects being placed onto tables and evaluates the classification and time-of-arrival estimation algorithms using a data set of human-generated sounds.
Journal ArticleDOI
Mobile learning in context
Hendrik Thüs,Mohamed Amine Chatti,Esra Yalcin,Christoph Pallasch,Bogdan Kyryliuk,Togrul Mageramov,Ulrik Schroeder +6 more
TL;DR: This paper explores how context can deliver significant benefits in mobile learning and provides an extensive review of the current literature and research on mobile learning in context and proposes the conceptual framework CAMeL for context-aware mobile learning.
Journal ArticleDOI
MSF: An Efficient Mobile Phone Sensing Framework
TL;DR: This paper proposes Mobile Sensing Framework (MSF), a flexible platform to ease the development of mobile sensing applications through the definition of a common set of facilities that mask all low-level technical details in reading and processing raw sensor data.
Dissertation
Sensing flow execution engine for concurrent mobile sensing applications = 동시에 동작하는 모바일 센싱 애플리케이션을 위한 센싱 플로우 실행 엔진
Young-Hyun Ju,주영현 +1 more
TL;DR: This work develops SymPhoney, a coordinated sensing flow execution engine to support concurrent sensing applications, and introduces the new concept of frame externalization i.e., to identify and externalize semantic structures embedded in otherwise flat sensing data streams.
Proceedings ArticleDOI
Cross-Platform Support for Rapid Development of Mobile Acoustic Sensing Applications
TL;DR: This paper implements apps covering three major acoustic sensing categories and demonstrates the benefits and simplicity of developing apps with LibAS, a cross-platform framework to facilitate the rapid development of mobile acoustic sensing apps.
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