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
More filters
Book ChapterDOI
Low-Power Ambient Sensing in Smartphones for Continuous Semantic Localization
TL;DR: Low-power ambient sensors are proposed to be integrated in phones to enable a continuous observation with minimal impact on power consumption, and achieve up to 80% accuracy for recognition of five location categories in a user-specific setting, while saving up to 85% of the battery power consumed by traditional sensing modalities.
Book ChapterDOI
Preserving query privacy in urban sensing systems
TL;DR: This is the first attempt to define and address both query and data privacy in the context of Urban Sensing, and proposes a distributed privacy-preserving technique that is tunable, trading off the level of privacy assurance with a small overhead increase.
Dissertation
Opportunistic mobile social networks at work
TL;DR: MobiClique as mentioned in this paper is a middleware designed for reseaux opportunistes, i.e., mobile ad-hoc networks. André et al. propose a methodologie d'analyse des structures des communautes temporelles dans le reseau opportuniste, which s'appuie sur la mobilite and les relations sociales des users.
Book ChapterDOI
Challenges and Opportunities in Automated Detection of Eating Activity
TL;DR: This chapter discusses the problem of automated eating detection and presents a variety of practical techniques for detecting eating activities in real-world settings that center on three sensing modalities: first-person images taken with wearable cameras, ambient sounds, and on-body inertial sensors.
Proceedings ArticleDOI
Beyond location check-ins: Exploring physical and soft sensing to augment social check-in apps
TL;DR: This paper shows how mobile phone sensing can be used in this sense and shows that when using only soft sensors the authors can achieve very similar performance to that obtained with real sensors, thereby significantly reducing the impact on the phone battery.
References
More filters
Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Book
Pattern Recognition and Machine Learning (Information Science and Statistics)
TL;DR: Looking for competent reading resources?
Book
Fundamentals of speech recognition
TL;DR: This book presents a meta-modelling framework for speech recognition that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually modeling speech.
Journal ArticleDOI
On the use of windows for harmonic analysis with the discrete Fourier transform
TL;DR: A comprehensive catalog of data windows along with their significant performance parameters from which the different windows can be compared is included, and an example demonstrates the use and value of windows to resolve closely spaced harmonic signals characterized by large differences in amplitude.