Sociofeedback by google glass

This project concentrates on the development of an Android application for Sociofeedback. The available real-time Sociofeedback system and applications requires a series of devices to analyse the audio input and perform classification. In this project, all the signal processing and classification...

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Main Author: Zhong, Sailin
Other Authors: Justin Dauwels
Format: Final Year Project
Language:English
Published: 2016
Subjects:
Online Access:http://hdl.handle.net/10356/67670
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-676702023-07-07T15:58:39Z Sociofeedback by google glass Zhong, Sailin Justin Dauwels School of Electrical and Electronic Engineering DRNTU::Engineering This project concentrates on the development of an Android application for Sociofeedback. The available real-time Sociofeedback system and applications requires a series of devices to analyse the audio input and perform classification. In this project, all the signal processing and classification are aimed to be accomplished on the mobile devices themselves. Two platforms are discussed in this report — Google Glass and Android mobile phone. The report illustrates the development process with reference to the system development life cycle (SDLC). The functionalities of the applications have been varying through the weekly meetings. This application initially takes monologues as audio input to identify the speech mannerisms and provide feedback in order to enhance users’ presentation skills. Thinking of distributing such application to broader audience, we start to accommodate the application for two-person. The Sociofeedback app can further fit into workplace such as call centres and police offices. Users are able to select their preferred low-level features to be monitored, including volume, pitch, speaking percentage, and MFCC so far. The selected low-level features will be shown graphically on the app. Twelves low-level features are derived from the volume, pitch and MFCC of the speech from the two users to perform classification. Google Glass is one of the most cutting-edge wirable devices in the market. It is light, unsophisticated and portable. Although the processing capability of Google Glass is not satisfying in this project, the future generation of such smart glass would be an appreciable platform for this app. We use Android phone as an alternative device to showcase the application. The iterative design procedure will be elaborated in this report. Bachelor of Engineering 2016-05-19T03:26:52Z 2016-05-19T03:26:52Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/67670 en Nanyang Technological University 59 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Zhong, Sailin
Sociofeedback by google glass
description This project concentrates on the development of an Android application for Sociofeedback. The available real-time Sociofeedback system and applications requires a series of devices to analyse the audio input and perform classification. In this project, all the signal processing and classification are aimed to be accomplished on the mobile devices themselves. Two platforms are discussed in this report — Google Glass and Android mobile phone. The report illustrates the development process with reference to the system development life cycle (SDLC). The functionalities of the applications have been varying through the weekly meetings. This application initially takes monologues as audio input to identify the speech mannerisms and provide feedback in order to enhance users’ presentation skills. Thinking of distributing such application to broader audience, we start to accommodate the application for two-person. The Sociofeedback app can further fit into workplace such as call centres and police offices. Users are able to select their preferred low-level features to be monitored, including volume, pitch, speaking percentage, and MFCC so far. The selected low-level features will be shown graphically on the app. Twelves low-level features are derived from the volume, pitch and MFCC of the speech from the two users to perform classification. Google Glass is one of the most cutting-edge wirable devices in the market. It is light, unsophisticated and portable. Although the processing capability of Google Glass is not satisfying in this project, the future generation of such smart glass would be an appreciable platform for this app. We use Android phone as an alternative device to showcase the application. The iterative design procedure will be elaborated in this report.
author2 Justin Dauwels
author_facet Justin Dauwels
Zhong, Sailin
format Final Year Project
author Zhong, Sailin
author_sort Zhong, Sailin
title Sociofeedback by google glass
title_short Sociofeedback by google glass
title_full Sociofeedback by google glass
title_fullStr Sociofeedback by google glass
title_full_unstemmed Sociofeedback by google glass
title_sort sociofeedback by google glass
publishDate 2016
url http://hdl.handle.net/10356/67670
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