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Proceedings ArticleDOI

SoundSense: scalable sound sensing for people-centric applications on mobile phones

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.

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Dissertation

Studying user behavior through a participatory sensing framework in an urban context

Ngo Manh Khoi
TL;DR: The design and implementation of a multi-purpose participatory sensing framework (Citizense) based on a list of requirements extracted from the literature enables ordinary users to create sensing campaigns and collect various types of data.
Journal ArticleDOI

A multi-agent architecture for mobile sensing systems

TL;DR: The main challenges in mobile sensing systems such as scalability in crowded environments, handling of a large amount of data and the increasing appearance of sensing devices are addressed by the architecture due to the agent paradigm and multi-agent systems suit these demands naturally.
Journal ArticleDOI

Sim2RealQA: Using Life Simulation to Solve Question Answering Real-World Events

TL;DR: A novel simulation to real QA (Sim2RealQA) framework is proposed that completely trains a QA model with QA datasets produced in a life simulator and is used for solving real-word QA problems without answer labels.
Proceedings ArticleDOI

Gunshot Classification and Localization System using Artificial Neural Network (ANN)

TL;DR: The purpose of the study is to aid soldiers in times of combat to avoid relatively large unpredictable loss and to safeguard the authors' territories from unconscious attacks by developing a gunshot classification and localization system.
Proceedings ArticleDOI

Not a tile out of place: Toward creating context-dependent user interfaces on smartglasses

TL;DR: A data representation model is proposed to combine applications and services that match user activities and contexts and it is suggested that combining those two aspects can open the way to personalized services for the end user, creating new ways of interacting with applications and devices.
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.
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Pattern Recognition and Machine Learning (Information Science and Statistics)

TL;DR: Looking for competent reading resources?
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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

F.J. Harris
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.
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