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Microphone identification using one-class classification approach
conference contribution
posted on 2011-01-01, 00:00 authored by Huy Quan Vu, Shaowu Liu, Z Li, Gang LiGang LiRapid 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.
History
Event
Applications and Techniques in Information Security Workshop (2nd : 2011 : Melbourne, Vic.)Pagination
30 - 37Publisher
Deakin University School of Information SystemsLocation
Melbourne, Vic.Place of publication
AustraliaStart date
2011-11-09ISBN-13
9780987229809Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2011, Deakin UniversityEditor/Contributor(s)
M WarrenTitle of proceedings
ATIS 2011 : Workshop proceedingof ATIS 2011. Melbourne, November 9th, 2011. Second Applications and Techniques in Information Security WorkshopUsage metrics
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