A sparsogram implementation for wildlife intruder detection

ISSCS 2013 - International Symposium on Signals, Circuits and Systems

Volumn , Issue , 2013, Pages

  Rosu, Roxana Gabriela ^a   Rusu, Corneliu ^a  

^a TECHNICAL UNIVERSITY OF CLUJ NAPOCA   (Romania)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION METHODS; DIFFERENT CLASS; DOMINANT FREQUENCY; FREQUENCY MEASUREMENTS; INTRUDER DETECTION; NATURAL ENVIRONMENTS; SPECTRAL COMPONENTS; SPECTROGRAMS;

ANIMALS; FREQUENCY BANDS; INTRUSION DETECTION; SPECTRUM ANALYSIS;

SIGNAL DETECTION;

EID: 84890883070     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISSCS.2013.6651260     Document Type: Conference Paper

References (7)

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3. F. Weninger and B. Schuller, Audio recognition in the wild: static and synamic classification on a real-world database of animal vocalizations, Institute of Human-Machine Communication, Technische Universitt Mnchen 80290 Mnchen, Germany, 2011.
4. Marius Ghiurcau, Sound classification in forensic scenarios, PhD Thesis, Universitatea Tehnica din Cluj-Napoca, 2011.
5. Background knowledge on spectrogram reading: http://www.cslu.ogi.edu/ tutordemos/SpectrogramReading/spectrogramreading.html.
6. T. Ragheb, S. Kirolos, J. Laska, A. Gilbert, M. Strauss, R. Baranuik, Y. Massoud, Implementation Models for Analog-to Information Conversion via Random Smapling, Department of Electrical and Computer Engineering, Rice University, Houston, TX, Departments of Mathematics and EECS, University of Michigan, Ann Arbour, MI.
7. Wikipedia: k-nearest neighbor algorithm: http://en.wikipedia.org/wiki/ Knearestneighboralgorith.