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
Classifying respiratory sounds using electronic stethoscope
TL;DR: A computer-based solution for automatic analysis of respiratory sounds captured using the stethoscope, which has many potential applications including telemedicine and self-screening, achieves the accuracy of 98.4%, which means the models could be used in the real-world situation for the diagnosis of pulmonary diseases.
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Mining Context-Aware User Requirements from Crowd Contributed Mobile Data
TL;DR: The approach captures behavior records contributed by a crowd of mobile users and automatically mines context-aware user behavior patterns from them using Apriori-M algorithm and finds solutions that satisfy the requirements and are recommended to users.
Journal ArticleDOI
User Environment Detection with Acoustic Sensors Embedded on Mobile Devices for the Recognition of Activities of Daily Living
Ivan Miguel Pires,Nuno M. Garcia,Nuno Pombo,Francisco Flórez-Revuelta,Susanna Spinsante,Maria Canavarro Teixeira,Eftim Zdravevski +6 more
TL;DR: In this paper, the authors used different types of Artificial Neural Networks (ANNs) for the identification of activities of daily living (ADL) and their environments, including motion and magnetic sensors.
Proceedings ArticleDOI
Conversational User Interfaces on Mobile Devices: Survey
Razan Jaber,Donald McMillan +1 more
TL;DR: A survey of mobile conversation user interface research since the commercial deployment of Apple's Siri, the first readily available consumer CUI, and the implications for CUIs of greater access to the context of use are presented.
Proceedings ArticleDOI
HARK-Bird-Box: A Portable Real-time Bird Song Scene Analysis System
TL;DR: This paper proposes a cascaded approach, cascading sound source detection, localization, separation, feature extraction, classification, and visualization for bird song analysis and implemented a bird song classifier based on a convolutional neural network (CNN).
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