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http://dx.doi.org/10.3745/KTSDE.2014.3.2.81

Design and Implementation of a Sound Classification System for Context-Aware Mobile Computing  

Kim, Joo-Hee (경기대학교 컴퓨터과학과)
Lee, Seok-Jun (경기대학교 컴퓨터과학과)
Kim, In-Cheol (경기대학교 컴퓨터과학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.3, no.2, 2014 , pp. 81-86 More about this Journal
Abstract
In this paper, we present an effective sound classification system for recognizing the real-time context of a smartphone user. Our system avoids unnecessary consumption of limited computational resource by filtering both silence and white noise out of input sound data in the pre-processing step. It also improves the classification performance on low energy-level sounds by amplifying them as pre-processing. Moreover, for efficient learning and application of HMM classification models, our system executes the dimension reduction and discretization on the feature vectors through k-means clustering. We collected a large amount of 8 different type sound data from daily life in a university research building and then conducted experiments using them. Through these experiments, our system showed high classification performance.
Keywords
Context-Aware Mobile Computing; Sound Classification; Hidden Markov Model;
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1 J. Chen, A. H. Kam, J. Zhang, et al., "Bathroom Activity Monitoring Based on Sound", Proc. of Int. Conf. on Pervasive Computing, pp.47-61, 2005.
2 A. J. Eronen, V. T. Peltonen, J. T. Tuomi, et al., "Audio-Based Context Recognition", IEEE Trans. on Audio, Speech, and Language Processing, Vol.14, No.1, pp.321-329, 2006.   DOI   ScienceOn
3 A. Mesaros, T. Heittola, A. Eronen, and T. Virtanen, "Acostic Event Detection in Real-Life Recordings", Proc. of European Signal Processing Conference, pp.1267-1271, 2010.
4 L. Ma, D. J. Smith, B. P. Milner, "Context Awareness Using Environmental Noise Classification", Proc. of EuroSpeech-03, Geneva, pp.2237-2340, 2003.
5 S. Teramoto, J. Noda, "O-MUSUBI: Ad-hoc Grouping System Enhanced by Ambient Sound-The Similarity based on Information Theoretical Features for Sound-Fields", Proc. of ICONS-2013, pp.52-58, 2013.
6 H. Lu, W. Pan, N. D. Lane, et al., "SoundSense: Scalable Sound Sensing for People-Centric Applications on Mobile Phones", Proc. of MobiSys-09, pp.165-178, 2009.
7 L. Lu, H. Jiang, H. Zhang, "A Robust Audio Classification and Segmentation Method", Proc. of ACM Multimedia, pp. 203-211, 2001.
8 M. Rossi, S. Feese, O. Amft, et al., "AmbientSense: A Real-Time Ambient Sound Recognition System for Smartphones", Proc. of IEEE International Workshop on the Impact of Human Mobility in Pervasive Systems and Applications(PerMoby-2013), pp.230-235, 2013.
9 H. Lu, J. Yang, Z. Liu, et al., "The Jigsaw Continuous Sensing Engine for Mobile Phone Applications", Proc. of SenSys-10, pp.71-84, 2010.