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25071 An Environmental Audio-Based Context Recognition System Using Smartphones
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Abreha, G.T (2014) An Environmental Audio-Based Context Recognition System Using Smartphones. Master's thesis, University of Twente.

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Abstract

Environmental sound/audio is a rich source of information that can be used to infer
a person's context in daily life. Almost every activity produces some sound patterns,
e.g., speaking, walking, washing, or typing on computer. Most locations have usually
a specific sound pattern too, e.g., restaurants, offices or streets. This thesis addresses
the design and development of an application for real-time detection and recognition
of user activities using audio signals on mobile phones. The audio recognition application
increases the capability, intelligence and feature of the mobile phones and, thus,
increases the convenience of the users. For example, a smartphone can automatically
go into a silent mode while entering a meeting or provide information customized to
the location of the user. However, mobile phones have limited power and capabilities
in terms of CPU, memory and energy supply. As a result, it is important that the design
of audio recognition application meets the limited resources of the mobile phones.
In this thesis we compare performance of different audio classifiers (k-NN, SVM and
GMM) and audio feature extraction techniques based on their recognition accuracy and
computational speed in order to select the optimal ones. We evaluate the performance
of the audio event recognition techniques on a set of 6 daily life sound classes (coffee
machine brewing, water tape (hand washing), walking, elevator, door opening/closing,
and silence ). Test results show that the k-NN classifier (when used with mel-frequency
cepstral coefficients (MFCCs), spectral entropy (SE) and spectral centroid (SC) audio
features) outperforms other audio classifiers in terms of recognition accuracy and execution
time. The audio features are selected based on simulation results and proved to be
optimal features. An online audio event recognition application is then implemented as an Android app (on mobile phones) using the k-NN classifier and the selected optimal
audio features. The application continuously classies audio events (user activities) by
analyzing environmental sounds sampled from smartphone's microphone. It provides a
user with real-time display of the recognized context (activity). The impact of other
parameters such as analysis window and overlapping size on the performance of audio
recognition is also analyzed. The test result shows that varying the parameters does not
have significant impact on the performance of the audio recognition technique. Moreover,
we also compared online audio recognition results of the same classifier set (i.e.,
k-NN) with that of the off-line classification results.

Item Type:Master's Thesis
Research Group:EWI-PS: Pervasive Systems
Research Program:CTIT-WiSe: Wireless and Sensor Systems
Research Project:Go Green: Greener House Through A Self-learning, Privacy-aware User-centric Energy-aware Wireless Monitoring And Control System
ID Code:25071
Deposited On:08 December 2014
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