Compressed sensing based acoustic event detection in protected area networks with wireless multimedia sensors

Wireless multimedia sensors have been frequently used for detecting events in acoustic rich environments such as protected area networks. Such areas have diverse habitat, frequently varying terrain and are a source of very large number of acoustic events. This work is aimed at detecting the tree cutting event in a forest area, by identifying the acoustic pattern generated due to an axe hitting a tree bole, with the help of wireless multimedia sensors. A series of operations using the hamming window, wiener filter, Otsu thresholding and mathematical morphology are used for removing the unwanted clutter from the spectrogram obtained from such events. Using the sparse nature of the acoustic signals, a compressed sensing based energy efficient data gathering scheme is devised for accurate event reporting. A network of Mica2 motes is deployed in a real forest area to test the validity of the proposed scheme. Analytical and experimental results proves the efficacy of the proposed event detection scheme.

[1]  Di Xiao,et al.  An efficient and noise resistive selective image encryption scheme for gray images based on chaotic maps and DNA complementary rules , 2014, Multimedia Tools and Applications.

[2]  Chenn-Jung Huang,et al.  Frog classification using machine learning techniques , 2009, Expert Syst. Appl..

[3]  Tushar Sandhan,et al.  Audio Bank: A high-level acoustic signal representation for audio event recognition , 2014, 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014).

[4]  Xiaoming Liu,et al.  Abnormal Event Detection Method in Multimedia Sensor Networks , 2015, Int. J. Distributed Sens. Networks.

[5]  Damjan Vlaj,et al.  Acoustic classification and segmentation using modified spectral roll-off and variance-based features , 2013, Digit. Signal Process..

[6]  Mustafa Sert,et al.  Audio-based event detection in office live environments using optimized MFCC-SVM approach , 2015, Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015).

[7]  T. Kadowaki,et al.  Evolution of TRP channels inferred by their classification in diverse animal species. , 2015, Molecular phylogenetics and evolution.

[8]  Xiang-Yang Li,et al.  Energy Efficient Target-Oriented Scheduling in Directional Sensor Networks , 2009, IEEE Transactions on Computers.

[9]  Tuomas Virtanen,et al.  Acoustic event detection in real life recordings , 2010, 2010 18th European Signal Processing Conference.

[10]  Len Thomas,et al.  A method for detecting whistles, moans, and other frequency contour sounds. , 2011, The Journal of the Acoustical Society of America.

[11]  Huy Phan,et al.  Random Regression Forests for Acoustic Event Detection and Classification , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[12]  Abdullah Al-Dhelaan,et al.  Image-Based Object Identification for Efficient Event-Driven Sensing in Wireless Multimedia Sensor Networks , 2015, Int. J. Distributed Sens. Networks.

[13]  Raja Datta,et al.  A two-tier strategy for priority based critical event surveillance with wireless multimedia sensors , 2016, Wirel. Networks.

[14]  Anand V. Panangadan,et al.  Extreme event detection and assimilation from multimedia sources , 2012, Multimedia Tools and Applications.

[15]  Jozef Juhár,et al.  Feature selection for acoustic events detection , 2013, Multimedia Tools and Applications.

[16]  Manish Kumar,et al.  Hierarchical Compressed Sensing for Cluster Based Wireless Sensor Networks , 2016 .

[17]  Musab Ghadi,et al.  Securing data exchange in wireless multimedia sensor networks: perspectives and challenges , 2014, Multimedia Tools and Applications.

[18]  Andrzej Czyzewski,et al.  Detection, classification and localization of acoustic events in the presence of background noise for acoustic surveillance of hazardous situations , 2015, Multimedia Tools and Applications.

[19]  Hanseok Ko,et al.  Acoustic signal based abnormal event detection in indoor environment using multiclass adaboost , 2013, IEEE Transactions on Consumer Electronics.

[20]  Andrzej Czyzewski,et al.  Detection and localization of selected acoustic events in acoustic field for smart surveillance applications , 2012, Multimedia Tools and Applications.

[21]  Turker Ince,et al.  Early Forest Fire Detection Using Radio-Acoustic Sounding System , 2009, Sensors.

[22]  Álvaro Araujo,et al.  Forest Monitoring and Wildland Early Fire Detection by a Hierarchical Wireless Sensor Network , 2016, J. Sensors.

[23]  Jimmy Ludeña-Choez,et al.  Feature extraction based on the high-pass filtering of audio signals for Acoustic Event Classification , 2015, Comput. Speech Lang..