Microphone identification using one-class classification approach

Vu, Huy Quan, Liu, Shaowu, Li, Zhi and Li, Gang 2011, Microphone identification using one-class classification approach, in ATIS 2011 : Workshop proceedingof ATIS 2011. Melbourne, November 9th, 2011. Second Applications and Techniques in Information Security Workshop, Deakin University School of Information Systems, Australia, pp. 30-37.

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Title Microphone identification using one-class classification approach
Author(s) Vu, Huy Quan
Liu, Shaowu
Li, Zhi
Li, Gang
Conference name Applications and Techniques in Information Security Workshop (2nd : 2011 : Melbourne, Vic.)
Conference location Melbourne, Vic.
Conference dates 9 Nov. 2011
Title of proceedings ATIS 2011 : Workshop proceedingof ATIS 2011. Melbourne, November 9th, 2011. Second Applications and Techniques in Information Security Workshop
Editor(s) Warren, Matthew
Publication date 2011
Conference series Applications and Techniques in Information Security Workshop
Start page 30
End page 37
Total pages 8
Publisher Deakin University School of Information Systems
Place of publication Australia
Keyword(s) machine learning
data mining
audio forensics
microphione forensics
one-class classification
Summary Rapid growth of technical developments has created huge challenges for microphone forensics - a subcategory of audio forensic science, because of the availability of numerous digital recording devices and massive amount of recording data. Demand for fast and efficient methods to assure integrity and authenticity of information is becoming more and more important in criminal investigation nowadays. Machine learning has emerged as an important technique to support audio analysis processes of microphone forensic practitioners. However, its application to real life situations using supervised learning is still facing great challenges due to expensiveness in collecting data and updating system. In this paper, we introduce a new machine learning approach which is called One-class Classification (OCC) to be applied to microphone forensics; we demonstrate its capability on a corpus of audio samples collected from several microphones. Research results and analysis indicate that OCC has the potential to benefit microphone forensic practitioners in developing new tools and techniques for effective and efficient analysis.
ISBN 9780987229809
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2011
Copyright notice ©2011, Deakin University
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044840

Document type: Conference Paper
Collection: School of Information Technology
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