Abstract
Home environments are one of the subjects of study regarding ambient intelligent systems for various purposes, including development of assistance systems for the elderly and energy consumption optimization. Sensing the environmental state via different sensors is the first and crucial component of every ambient intelligent system. In this work we investigate the use of environmental sounds for touch-free audio-based device recognition in a home environment. For this purpose, we analyzed sound characteristics of typical home appliances using different processing techniques. We are using the acquired knowledge to develop a flexible set of features, which can be set manually or determined automatically. To classify the device-specific acoustic fingerprints – consisting of a significant subset of our features – we use established supervised learning techniques, whereby we optimized the straightforward ones. After building a recognition basis for the recognition of fixed length sound buffers on demand, we implemented a live recognition mode for real-time environment monitoring, providing runtime setup adjustments. We then extended our work with the recognition of untrained, simultaneously working, known devices by mixing their records, utilizing semi-supervised learning. We then anticipated promising results in our evaluation in various aspects, including recognition rate, performance for the different combinations of features, as well as to study the reliability of an automatic mixing of trained data.
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References
Belley, C., Gaboury, S., Bouchard, B., Bouzouane, A.: Activity Recognition in Smart Homes Based on Electrical Devices Identification. In: Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 7:1–7:8. ACM, Island of Rhodes (2013)
Brey, P.: Freedom and Privacy in Ambient Intelligence. Ethics and Information Technology 7(3), 157–166 (2005)
Chu, S., Narayanan, S., Kuo, C.-C.: Environmental Sound Recognition With Time-Frequency Audio Features. IEEE Transactions on Audio, Speech, and Language Processing 17(6), 1142–1158 (2009)
Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient Intelligence: Technologies, Applications, and Opportunities. Pervasive and Mobile Computing 5(4), 277–298 (2009)
Gupta, S., Reynolds, M.S., Patel, S.N.: ElectriSense: single-point sensing using EMI for electrical event detection and classification in the home. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, pp. 139–148. ACM, New York (2010)
Hart, G.: Residential energy monitoring and computerized surveillance via utility power flows. Technology and Society Magazine 8(2), 12–16 (1989)
Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning (2nd ed.). Springer (2009)
Herbrich, R., Graepel, T., Campbell, C.: Bayes point machines. The Journal of Machine Learning Research 1, 245–279 (2001)
Istrate, D., Vacher, M., Serignat, J.-F.: Embedded Implementation of Distress Situation. Identification Through Sound Analysis. The. Journal on Information Technology in Healthcare 6(3), 204–211 (2008)
Karbasi, M., Ahadi, S., Bahmanian, M.: Environmental sound classification using spectral dynamic features. In: 8th International Conference on Information, Communications and Signal Processing, ICICS, Singapore, pp. 1–5 (December 13-16, 2011)
Ke, S.-R., Hwang, J.-N., Yoo, J.-H., Choi, K.-H.: A review on video-based human activity recognition. Computers 2(2), 88–131 (2013)
Lozano, H., Hernáez, I., Picón, A., Camarena, J., Navas, E.: Audio Classification Techniques in Home Environments for Elderly/Dependant People. In: Miesenberger, K., Klaus, J., Zagler, W., Karshmer, A. (eds.) ICCHP 2010, Part 1. LNCS, vol. 6179, pp. 320–323. Springer, Heidelberg (2010)
Minka, T.P.: Expectation Propagation for Approximate Bayesian Inference. In: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, UAI 2001, pp. 362–369. Morgan Kaufmann Publishers Inc., San Francisco (2001)
Minka, T., Winn, J., Guiver, J., Knowles, D.: Infer.NET 2.5. Microsoft Research Cambridge (2012)
Müller, M., Ellis, D.P., Klapuri, A., Richard, G.: processing for music analysis. IEEE Journal on Selected Topics in Signal Processing 5(6), 1088–1110 (2011)
Sehili, M.A., Lecouteux, B., Vacher, M., Portet, F., Istrate, D., Dorizzi, B., Boudy, J.: Sound Environment Analysis in Smart Home. In: Paternò, F., de Ruyter, B., Markopoulos, P., Santoro, C., van Loenen, E., Luyten, K. (eds.) AmI 2012. LNCS, vol. 7683, pp. 208–223. Springer, Heidelberg (2012)
Stäger, M.: Low-Power Sound-Based User Activity Recognition. Swiss Federal Institute of Technology Zurich, Information Technology and Electrical Engineering. ETH Zürich, Zürich (2006)
Stager, M., Lukowicz, P., Troster, G.: Implementation and Evaluation of a Low-Power Sound-Based User Activity Recognition System. In: Proceedings of the Eighth International Symposium on Wearable Computers, pp. 138–141. IEEE Computer Society, Washington, DC (2004)
Stager, M., Lukowicz, P., Troster, G.: Power and accuracy trade-offs in sound-based context recognition systems. PerCom -. Pervasive and Mobile Computing 3(3), 300–327 (2007)
Temko, A., Malkin, R., Zieger, C., Macho, D., & Nadeu, C.: Acoustic Event Detection and Classification in Smart-Room Environments: Evaluation of CHIL Project Systems. Cough, 5–11 (2006)
Wang, J.-C., Lee, H.-P., Wang, J.-F., Lin, C.-B.: January 4). Robust Environmental Sound Recognition for Home Automation. IEEE Transactions on Automation Science and Engineering 5(1), 25–31 (2008)
Weber, W., Rabaey, J., Aarts, E.H.: Ambient Intelligence. Springer (March, 2005)
Wieczorkowska, A., Kolczyńska, E., Raś, Z.W.: Training of Classifiers for the Recognition of Musical Instrument Dominating in the Same-Pitch Mix. New Challenges in Applied Intelligence Technologies III(134), 213–222 (2008)
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Dimitrov, S., Britz, J., Brandherm, B., Frey, J. (2014). Analyzing Sounds of Home Environment for Device Recognition. In: Aarts, E., et al. Ambient Intelligence. AmI 2014. Lecture Notes in Computer Science(), vol 8850. Springer, Cham. https://doi.org/10.1007/978-3-319-14112-1_1
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DOI: https://doi.org/10.1007/978-3-319-14112-1_1
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