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
Journal ArticleDOI
Exploiting Mobile Devices to Assist Identification System of Photos for Social Relationship
Xin Mao Huang,Ke Yi Kuo,Yi Chiou +2 more
TL;DR: A mobile device-assisted determining social relationship system, which mainly uses the phone to collect geographic information about the surrounding people to determine whether they are within the angle of view of the mobile device's camera.
Proceedings ArticleDOI
Recognizing extended surrounding contexts via class incremental learning
TL;DR: Results of the experiments show that FCI-ELM can automatically and continuously recognize newly discovered classes of contexts in the real-world, and the model can keep the same recognition ability by class incremental learning approach.
Complex event recognition through wearable sensors
TL;DR: This thesis proposes a hierarchical approach to detect simple events from the wearable sensors data and then build complex events on top of them and focuses on the detection of glycemic events for diabetes type-1 patients in a non-invasive manner.
Book ChapterDOI
Panorama: A Framework to Support Collaborative Context Monitoring on Co-Located Mobile Devices
TL;DR: Panorama as mentioned in this paper is a middleware that identifies collaboration opportunities to offload context computing tasks to nearby mobile devices as well as cloudlets/cloud, and computes a collaboration plan optimized simultaneously for different objectives such as minimizing cost, energy and/or execution time.
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.