Article

Lifetrak: music in tune with your life

Authors:

Jeff MasciaAuthors Info & Claims

HCM '06: Proceedings of the 1st ACM international workshop on Human-centered multimedia

Pages 25 - 34

https://doi.org/10.1145/1178745.1178754

Published: 27 October 2006 Publication History

Abstract

Advances in sensing technology and wider availability of network services is beckoning the use of context-awareness in ubiquitous computing applications. One region in which these technologies can play a major role is in the area of entertainment. Particularly, context-awareness can be used to provide higher quality interaction between humans and the media they are interacting with. We propose a music player, Lifetrak, that is in tune with a person's life by using a context-sensitive music engine to drive what music is played. This context engine is influenced by (i) the location of the user, (ii) the time of operation, (iii) the velocity of the user, and (iv) urban environment information such as traffic, weather, and sound modalities. Furthermore, we adjust the context engine by implementing a learning model that is based on user feedback on whether a certain song is appropriate for a particular context. Also, we introduce the idea of a context equalizer that adjusts how much a certain sensing modality affects what song is chosen. Since the music player will be implemented on a mobile device, there is a strong focus on creating a user interface that can be manipulated by users on the go. The goal of Lifetrak is to liberate a user from having to consciously specify the music that they want to play. Instead, Lifetrak intends to create a music experience for the user that is in rhythm with themselves and the space they reside in.

References

[1]

K. Blanchette. Effects of MP3 Technology on the Music Industry: An Examination of Market Structure and Apple iTunes. College of the Holy Cross. Retrieved September, 30:2004, 2004.

[2]

Apple. Apple ipod family. http://www.apple.com/ipod, 2006.

[3]

S.I. Tamminen, A.I. Oulasvirta, K.I. Toiskallio, and A.I. Kankainen. Understanding mobile contexts. Personal and Ubiquitous Computing, 8(2):135--143, 2004.

[4]

Sony. Sony walkman bean mp3 player. http://www.sonystyle.com/walkman, 2006.

[5]

G.T. Elliott and B. Tomlinson. Personalsoundtrack: Context-aware playlists that adapt to user pace. In Conference on Human Factors in Computing Systems, pages 298--304. CHI, April 2006.

Digital Library

[6]

G. Wijnalda, S. Pauws, F. Vignoli, and H. Stuckenschmidt. A personalized music system for motivation in sport performance. Pervasive Computing, IEEE, 4(3):26--32, 2005.

Digital Library

[7]

P. Riddle. Tags: What are They Good For? http://www.ischool.utexas.edu, 2005.

[8]

J. Crossett. Social Classification of Data. http://jamescorssett.com, 2006.

[9]

LastFM. Last.fm, the social music revolution. http://www.last.fm, 2006.

[10]

R. Want, B.N. Schilit, N.I. Adams, R. Gold, K. Petersen, D. Goldberg, J.R. Ellis, and M. Weiser. An overview of the PARCTAB ubiquitous computing experiment. IEEE Personal Communications, 2(6):28--43, 1995.

[11]

G.D.B. Abowd, C.G.B. Atkeson, J.B. Hong, S.B. Long, R.B. Kooper, and M.B. Pinkerton. Cyberguide: A mobile context-aware tour guide. Wireless Networks, 3(5):421--433, 1997.

Digital Library

[12]

M.S. Pandit and S. Kalbag. The selection recognition agent: instant access to relevant information and operations. Proceedings of the 2nd international conference on Intelligent user interfaces, pages 47--52, 1997.

Digital Library

[13]

A.K. Dey. Context-aware computing: The CyberDesk project. Proceedings of the AAAI 1998 Spring Symposium on Intelligent Environments, pages 51--54, 1998.

[14]

P. Dourish. Seeking a Foundation for Context-Aware Computing. Human-Computer Interaction, 16(2, 3 & 4):229--241, 2001.

Digital Library

[15]

JA Landay and TR Kaufmann. User interface issues in mobile computing. Workstation Operating Systems, 1993. Proceedings., Fourth Workshop on, pages 40--47, 1993.

[16]

S. Sarker and J.D. Wells. Understanding mobile handheld device use and adoption. Communications of the ACM, 46(12):35--40, 2003.

Digital Library

[17]

Nokia. Nokia 770. http://www.nokia.com/770, 2006.

Cited By

Gabbolini GBridge D(2024)Surveying More Than Two Decades of Music Information Retrieval Research on PlaylistsACM Transactions on Intelligent Systems and Technology10.1145/368839815:6(1-68)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3688398
Sreenivasa BNirmala C(2022)Hybrid time centric recommendation model for e-commerce applications using behavioral traits of userInformation Technology and Management10.1007/s10799-022-00358-824:2(133-146)Online publication date: 15-Mar-2022
https://doi.org/10.1007/s10799-022-00358-8
Sheth NAgrawal RVishwakarma S(2022)A Content-Based Music Recommendation System Using RapidMinerIntelligent Computing Techniques for Smart Energy Systems10.1007/978-981-19-0252-9_36(395-406)Online publication date: 14-Jun-2022
https://doi.org/10.1007/978-981-19-0252-9_36
Show More Cited By

Index Terms

Lifetrak: music in tune with your life
1. Applied computing
  1. Arts and humanities
    1. Sound and music computing

Recommendations

GeoTracks: Adaptive Music for Everyday Journeys
MM '16: Proceedings of the 24th ACM international conference on Multimedia

Listening to music on the move is an everyday activity for many people. This paper proposes geotracks and geolists, music tracks and playlists of existing music that are aligned and adapted to specific journeys. We describe how everyday walking journeys ...
Impact of user context on song selection
MM '08: Proceedings of the 16th ACM international conference on Multimedia

The rise of digital music has led to a parallel rise in the need to manage music collections of several thousands of songs on a single device. Manual selection of songs for a music listening experience is a cumbersome task. In this paper, we present an ...
ZOOZbeat: mobile music recreation
CHI EA '10: CHI '10 Extended Abstracts on Human Factors in Computing Systems

ZOOZbeat is a gesture-based Music reCreation studio. It is designed to provide users with expressive and creative access to music making on the go. ZOOZbeat users can compose user-generated songs based on generic beats in different styles or remix and ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

HCM '06: Proceedings of the 1st ACM international workshop on Human-centered multimedia

October 2006

138 pages

ISBN:1595935002

DOI:10.1145/1178745

General Chairs:
Daniel Gatica-Perez
IDIAP Research Institute, Switzerland
,
Alejandro Jaimes
FXPAL Japan, Fuji Xerox, Co., Ltd., Japan
,
Nicu Sebe
University of Amsterdam, The Netherlands

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2006

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

MM06

Sponsor:

MM06: The 14th ACM International Conference on Multimedia 2006

October 27, 2006

California, Santa Barbara, USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

45
Total Citations
View Citations
1,019
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)0

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Gabbolini GBridge D(2024)Surveying More Than Two Decades of Music Information Retrieval Research on PlaylistsACM Transactions on Intelligent Systems and Technology10.1145/368839815:6(1-68)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3688398
Sreenivasa BNirmala C(2022)Hybrid time centric recommendation model for e-commerce applications using behavioral traits of userInformation Technology and Management10.1007/s10799-022-00358-824:2(133-146)Online publication date: 15-Mar-2022
https://doi.org/10.1007/s10799-022-00358-8
Sheth NAgrawal RVishwakarma S(2022)A Content-Based Music Recommendation System Using RapidMinerIntelligent Computing Techniques for Smart Energy Systems10.1007/978-981-19-0252-9_36(395-406)Online publication date: 14-Jun-2022
https://doi.org/10.1007/978-981-19-0252-9_36
Dimitrakopoulos GPanagiotopoulos I(2021)In-Vehicle Infotainment Systems: Using Bayesian Networks to Model Cognitive Selection of Music GenresIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.299700322:11(6900-6909)Online publication date: Nov-2021
https://doi.org/10.1109/TITS.2020.2997003
Sánchez-Moreno DZheng YMoreno-García M(2020)Time-Aware Music Recommender Systems: Modeling the Evolution of Implicit User Preferences and User Listening Habits in A Collaborative Filtering ApproachApplied Sciences10.3390/app1015532410:15(5324)Online publication date: 31-Jul-2020
https://doi.org/10.3390/app10155324
Ben Sassi IBen Yahia S(2020)How does context influence music preferences: a user-based study of the effects of contextual information on users’ preferred musicMultimedia Systems10.1007/s00530-020-00717-xOnline publication date: 21-Nov-2020
https://doi.org/10.1007/s00530-020-00717-x
Sanchez-Moreno DZheng YMoreno-Garcia M(2018)Incorporating Time Dynamics and Implicit Feedback into Music Recommender Systems2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2018.00-34(580-585)Online publication date: Dec-2018
https://doi.org/10.1109/WI.2018.00-34
Selvi CSivasankar E(2018)An Efficient Context-Aware Music Recommendation Based on Emotion and Time ContextData Science and Big Data Analytics10.1007/978-981-10-7641-1_18(215-228)Online publication date: 2-Aug-2018
https://doi.org/10.1007/978-981-10-7641-1_18
Tezcan PBakker SEggen BMival OSmyth MDalsgaard P(2017)MusicoProceedings of the 2017 ACM Conference Companion Publication on Designing Interactive Systems10.1145/3064857.3079131(121-126)Online publication date: 10-Jun-2017
https://dl.acm.org/doi/10.1145/3064857.3079131
Jannach DLerche LBonnin G(2017)Empfehlungssysteme, automatische Erzeugung von Wiedergabelisten und MusikdatenbankenHandbuch Funktionale Musik10.1007/978-3-658-14362-6_5-1(1-37)Online publication date: 4-Jan-2017
https://doi.org/10.1007/978-3-658-14362-6_5-1
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten