Abstract
Due to wireless sensor networks can transmit data wirelessly and can be disposed easily, they are used in the wild to monitor the change of environment. However, the lifetime of sensor is limited by the battery, especially when the monitored data type is audio, the lifetime is very short due to a huge amount of data transmission. By intuition, sensor mote analyzes the sensed data and decides not to deliver them to server that can reduce the expense of energy. Nevertheless, the ability of sensor mote is not powerful enough to work on complicated methods. Therefore, it is an urgent issue to design a method to keep analyzing speed and accuracy under the restricted memory and processor. This research proposed an embedded audio processing module in the sensor mote to extract and analyze audio features in advance. Then, through the estimation of likelihood of observed animal sound by the frequencies distribution, only the interesting audio data are sent back to server. The prototype of WSN system is built and examined in the wild to observe frogs. According to the results of experiments, the energy consumed by sensors through our method can be reduced effectively to prolong the observing time of animal detecting sensors.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A Survey on Sensor Networks. IEEE Communications Magazine 40(8), 102–114 (2002)
Gazor, S., Zhang, W.: Speech probability distribution. IEEE Signal Processing Letters 10(7), 204–207 (2003)
Hill, J., Culler, D.: A wireless embedded sensor architecture for system-level optimization. In: UC Berkeley Technical Report (2002)
Mainwaring, A., Polastre, J., Szewczyk, R., Culler, D., Anderson, J.: Wireless Sensor Networks for Habitat Monitoring. In: Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications (2002)
Pottie, G.J.: Wireless sensor networks. In: IEEE Information Theory Workshop, pp. 139–140 (1998)
Quatieri, T.F.: Disctete-Time Speech Signal Processing. Prentice Hall, Inc., Englewood Cliffs (2002)
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. IEEE Transactions on ASSP 77(2), 257–286 (1989)
Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker Verification Using Adapted Gaussian Mixture Models. Digital Signal Processing 10, 19–41 (2000)
Tilak, S., Abu-Ghazaleh, N.B., Heinzelman, W.: A taxonomy of wireless micro-sensor network models. ACM SIGMOBILE Mobile Computing and Communications Review 6(2), 28–36 (2002)
Wang, A., Chandrakasan, A.: Energy-Efficient DSPs for Wireless Sensor Networks. IEEE Signal Processing Magazine 19(4), 68–78 (2002)
Wu, B.F., Wang, K.C.: A Robust Endpoint Detection Algorithm Based on the Adaptive Band-Partitioning Spectral Entropy in Adverse Environments. IEEE Transactions on Speech and Audio Processing 13(5), 762–775 (2005)
Wu, C.H., Chen, J.H.: Speech activated telephony email reader (SATER) based on speaker verification and text-to-speech conversion. IEEE Transactions On Consumer Electronics 43(3), 707–716 (1997)
Xing, B., et al.: Short-time Gaussianization for robust speaker verification. In: Proceedings of ICASSP, vol. 1, pp. 681–684 (2002)
Yao, Y., Gehrke, J.: Query processing for sensor networks. In: Proceedings of the CIDR, pp. 233–244 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, NH., Wu, CA., Hsieh, SJ. (2009). Long-Term Animal Observation by Wireless Sensor Networks with Sound Recognition. In: Liu, B., Bestavros, A., Du, DZ., Wang, J. (eds) Wireless Algorithms, Systems, and Applications. WASA 2009. Lecture Notes in Computer Science, vol 5682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03417-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-03417-6_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03416-9
Online ISBN: 978-3-642-03417-6
eBook Packages: Computer ScienceComputer Science (R0)