First presentation williamjens

Table Selecta

Jens Pelgrims

William De Keyzer

• Introduction

• Problem

• Research question

• Objectives

• Literature study

• Real-life observations

• App

• Planning

• Workload

• Introduction

• Problem

• Research question

• Objectives

• Literature study

• Real-life observations

• App

• Planning

• Workload

Who are we

• Jens Pelgrims and William De Keyzer

• Master of science:

– Master in de toegepaste informatica

• Mentor: Gonzalo Alberto Parra Chico

Context

• Music

• Public Location

• Smartphone

• Tabletop

• Computer

Why we chose this subject

• Music

• Nightlife

• We would use it

• Smartphone => Android

• Introduction

• Problem

• Research question

• Objectives

• Literature study

• Real-life observations

• App

• Planning

• Workload

Problem

• DJ isn’t able to connect with crowd

• People don’t feel involved in music choice

• Music is just background “noise”

• Bars don’t really care about music, they just want profits

– People having fun tend to consume more

Problem

• People don’t want same things on party as in bar

• No DJ in bar

• Different context

– party

– bar

• Introduction

• Problem

• Research question

• Objectives

• Literature study

• Real-life observations

• App

• Planning

• Workload

Research Questions

• Could we improve the atmosphere in a public space by allowing the crowd to influence the music?

• Can this be done with an app on a mobile phone?

• How will we make this communication?

• Would it be good to give (partial) control of the music in a public space to the users?

• Do we need some form of control?

• Introduction

• Problem

• Research question

• Objectives

• Literature study

• Real-life observations

• App

• Planning

• Workload

Objectives

• Provide users the option to “aid” in music choice let their voice be heard

• Provide DJ’s with better ways to connect with the crowd

• Specifically– Information about the crowd’s preferences

• Exact statistics about genres and how many users like it

– Requests• The ability to request songs• The ability to view requested songs

– Voting• Votes can be made by the DJ• Users can then vote on it

Two versions

• Party

– DJ has control, but wants interact with the crowd

• Bar/Home

– Computer with music database

– Users have more control

– Autonomously

Version 1: Party

• App for smartphone

• Not central (overwhelming activity) at party

• Users feel involved

• DJ has control nevertheless

Version 2: Bar/Home

• App for smartphone

• Computer plays music, works autonomously

• Users have more control

• Introduction

• Problem

• Research question

• Objectives

• Literature study

• Real-life observations

• App

• Planning

• Workload

Music Listening personal

• People listen alone

• Loan CD

• Tell others about artists/songs

- Seeburger, D.2012, The Sound of Music: Sharing Song Selections between Collocated Strangers in Public Urban Places

- Baur, D.2012,Listening Factors: A Large-Scale Principal Components Analysis of Long-Term Music Listening Histories

- Camurri, D.2008,Active and Personalized Experience of Sound and Music Content

Music Listening sharing

• People want to share what they like

– (Facebook/Twitter)

• Collaborative listening is complex

- Purgina, D.2013,An Approach for Developing a Mobile Accessed Music Search Integration Platform

- Chao, D.2005Adaptive Radio: Achieving Consensus Using Negative Preferences

- Cunningham, D.2009,Exploring Soial Music Behavior: an Investigation of Music Selection at Parties

- Crossen, D.2002,Flytrap: Intelligent Group Music Recommendation

- Liu, D,2008,Social Playlist: Enabling Touch Points and Enriching Ongoing Relationships Through Collaborative Mobile Music Listening

Collaborative Listening

• Online profiles

• Dedicated computer

• Tabletop

• Shared screen

• Seperate decives

• Online playlist

Online profile

Dedicated Computer

• Computer in different room

• Leave your group

- Sprague, D. 2008,Music Selection using the PartyVote Democratic Jukebox

Tabletop

- Stavness , D,2005,The MUSICtable: A Map-based Ubiquitous system for Social Interaction with a digital music Collection

- F. Julia, D,2009,SongExplorer: a tabletop application for exploring large collections of songs

Shared Screen

- Cunningham, D,2009,Exploring social music behaviour: An investigation of music selection at parties

- Kukka, D,2009,UbIRockMachine: A Multimodel Music Voting Service for Shared Urban Spaces

Seperate Device

- Lipson, D,2004,Jukola: Democratic Music Choice in a Public Space

Online playlistTuneTug

http://www.tunetug.com

• Introduction

• Problem

• Research question

• Objectives

• Literature study

• Real-life observations

• App

• Planning

• Workload

Party

• Have a good time

• Know which song is played

• Request songs

• Ask for other genre

Bar

• Have conversation

• Listen to music in background

• Ask friends which song it is

• On facebook/foursquare/... with smartphone

• Make request at barkeeper

• Introduction

• Problem

• Research question

• Objectives

• Literature study

• Real-life observations

• App

• Planning

• Workload

Functionalities

• App-side– Give preferences– Vote– Make request

• Server-side– View preferences– Start vote– View requests

• Big screen– Dashboard

First Paper Prototype

Dashboard

Feedback

• SUS: 84%

• Dashboard has more potential

• Request screen not clear enough

• More feedback on the app

Second Prototype

Votes

Votes3 new screens instead of 1

Requests

Requests2 screens instead of 1

Digital Prototype

• Introduction

• Problem

• Research question

• Objectives

• Literature study

• Real-life observations

• App

• Planning

• Workload

Planning

• December: Continue with digital prototype

• January: Exams + digital prototype + writing

• February: Finish digital prototype + start testing

• March: Further testing of digital prototype + make adaptations if necessary+ test second digital prototype

• April: Test second digital prototype (finish)+ last adaptations + writing

• May: Writing

Planning – Future Questions

• Is server-client structure ideal?

• Is working on the same LAN good?

• Would autocomplete work best with sending data to server, or with local data?

• Will the app be responsive enough?

• How can we minimize the communication between the server and app

• Introduction

• Problem

• Research question

• Objectives

• Literature study

• Real-life observations

• App

• Planning

• Workload

Workload

Total hours worked: 274Expected score: 13/20

Thank you for listening

• Any questions?