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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iLearn: The App that Learns
TL;DR: iLearn is described, an application that embodies the Program-by-Demonstration paradigm on the Android platform that is able to "demonstrate" an activity to the phone that attempts to learn it, and recognize every subsequent occurrence of this particular activity.
Proceedings ArticleDOI
Distributed analytics for audio sensing applications
TL;DR: A system for performing predictive analytics on audio data, where the training is executed on the cloud and the classification can be executed at the edge, and the performance tradeoff of executing analytics at contemporary edge devices versus the cloud is quantified.
Learning Individuals’ Patterns and Contextual Events with Mobile Data Streams
TL;DR: The solution, RoutineSense, predicts the consistency of individuals’ daily routines from the data that smartphones can passively collect, and uses a novel method of identifying passively-detectable recurring events which, along with their expected time offsets with events of interest, can be used to predict the times of these daily events.
Dissertation
Dynamic service orchestration in the IP multimedia subsystem
TL;DR: The current state of research in this area is described, discussing the advantages and disadvantages of current systems, as well as proposing a novel design that aims to address the traditional problem of service conflict detection and resolution.
References
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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.