Towards scalable activity recognition: Adapting zero-effort crowdsourced acoustic models

Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia, MUM 2013

Volumn , Issue , 2013, Pages

  Nguyen Dinh, Long Van ^a   Blanke, Ulf ^a   Tröster, Gerhard ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

Active learning; Activity recognition; Adaptation; Ambient sound; Crowdsourcing; Mobile phone; Personalization; Semi supervised learning

Indexed keywords

ACTIVE LEARNING; ACTIVITY RECOGNITION; ADAPTATION; AMBIENT SOUNDS; CROWDSOURCING; PERSONALIZATIONS; SEMI-SUPERVISED LEARNING;

CELLULAR TELEPHONES; MOBILE PHONES; PATTERN RECOGNITION; SUPERVISED LEARNING;

E-LEARNING;

EID: 84893414442     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2541831.2541832     Document Type: Conference Paper

References (26)

1. V. Akkermans, F. Font, J. Funollet, B. DeJong, G. Roma, S. Togias, and X. Serra. Freesound 2: An improved platform for sharing audio clips. In International Society for Music Information Retrieval Conference (ISMIR 2011), Late-breaking Demo Session, Miami, Florida, USA, 2011.
2. J. Aucouturier, B. Defreville, and F. Pachet. The bag-of-frames approach to audio pattern recognition: A suficient model for urban soundscapes but not for polyphonic music. J Acoust Soc Am, 122(2):881, 2007.
3. L. Bao and S. S. Intille. Activity recognition from user-annotated acceleration data. In Pervasive Computing: Proc. of the 2nd Int'l Conference, 2004.
4. U. Blanke and B. Schiele. Daily routine recognition through activity spotting. In Location and Context Awareness, volume 5561 of Lecture Notes in Computer Science, pages 192-206. Springer Berlin Heidelberg, 2009.
5. R. S. Bucks, D. L. Ashworth, G. K. Wilcock, and K. Siegfried. Assessment of activities of daily living in dementia: Development of the bristol activities of daily living scale. Age and ageing, 25, Mar. 1996.
6. S. Chu, S. Narayanan, and C.-C. J. Kuo. Environmental sound recognition with time-frequency audio features. Trans. Audio, Speech and Lang. Proc., 17(6):1142-1158, Aug. 2009.
7. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical society, Series B, 39(1):1-38, 1977.
8. A. Eronen, V. Peltonen, J. Tuomi, A. Klapuri, S. Fagerlund, T. Sorsa, G. Lorho, and J. Huopaniemi. Audio-based context recognition. IEEE Transactions on Audio, Speech, and Language Processing, 2006.
9. S. Katz. Assessing self-maintenance: Activities of daily living, mobility, and instrumental activities of daily living. Journal of the American Geriatrics Society, 31(12), Dec. 1983.
10. N. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. Campbell. A survey of mobile phone sensing. IEEE Communications Magazine, 48, Sept. 2010.
11. H. Lu, W. Pan, N. D. Lane, T. Choudhury, and A. T. Campbell. Soundsense: Scalable sound sensing for people-centric applications on mobile phones. In Proceedings of the 7th international conference on Mobile systems, applications, and services, MobiSys '09, pages 165-178, 2009.
12. T. H. Monk, E. Frank, J. M. Potts, and D. J. Kupfer. A simple way to measure daily lifestyle regularity. In European Sleep Research Society, 2002.
13. L.-V. Nguyen-Dinh, M. Rossi, U. Blanke, and G. Tróster. Combining crowd-generated media and personal data: Semi-supervised learning for context recognition. In Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia, PDM '13, 2013.
14. K. Nigam, A. K. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using EM. Mach. Learn., 39(2-3), May 2000.
15. V. Peltonen, J. Tuomi, A. Klapuri, J. Huopaniemi, and T. Sorsa. Computational auditory scene recognition. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), volume 2, may 2002.
16. M. Perkowitz, M. Philipose, K. Fishkin, and D. J. Patterson. Mining models of human activities from the web. In Proceedings of the 13th international conference on World Wide Web, WWW '04, 2004.
17. D. A. Reynolds, T. F. Quatieri, and R. B. Dunn. Speaker verification using adapted Gaussian mixture models. In Digital Signal Processing, page 2000, 2000.
18. R. Rose and D. Reynolds. Text independent speaker identification using automatic acoustic segmentation. In International Conference on Acoustics, Speech, and Signal Processing, 1990, pages 293-296 vol.1, 1990.
19. M. Rossi, O. Amft, and G. Tröster. Recognizing daily life context using web-collected audio data. In the 16th IEEE International Symposium on Wearable Computers, 2012, June 2012.
20. B. Settles. Active learning literature survey. Computer sciences technical report, University of Wisconsin-Madison, 2010.
21. C. E. Shannon. A mathematical theory of communication. The Bell System Technical Journal, 27:379-423, 623-656, July, October 1948.
22. M. Stäger, N. Perera, and T. V. Büren. Soundbutton: Design of a low power wearable audio classification system. In the 7th International Symposium on Wearable Computers, 2003.
23. M. Stikic, K. Van Laerhoven, and B. Schiele. Exploring semi-supervised and active learning for activity recognition. In the 12th IEEE International Symposium on Wearable Computers, pages 81-88, 2008.
24. L. Van Der Maaten and G. Hinton. Visualizing high-dimensional data using t-sne. Journal of Machine Learning Research, 2008.
25. Z. Zhang and B. Schuller. Semi-supervised learning helps in sound event classification. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012.
26. X. Zhu. Semi-supervised learning literature survey. Technical report, Computer Sciences, University of Wisconsin-Madison, 2005.