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Proceedings Paper

A context model for microphone forensics and its application in evaluations
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Paper Abstract

In this paper we first design a suitable context model for microphone recordings, formalising and describing the involved signal processing pipeline and the corresponding influence factors. As a second contribution we apply the context model to devise empirical investigations about: a) the identification of suitable classification algorithms for statistical pattern recognition based microphone forensics, evaluating 74 supervised classification techniques and 8 clusterers; b) the determination of suitable features for the pattern recognition (with very good results for second order derivative MFCC based features), showing that a reduction to the 20 best features has no negative influence to the classification accuracy, but increases the processing speed by factor 30; c) the determination of the influence of changes in the microphone orientation and mounting on the classification performance, showing that the first has no detectable influence, while the latter shows a strong impact under certain circumstances; d) the performance achieved in using the statistical pattern recognition based microphone forensics approach for the detection of audio signal compositions.

Paper Details

Date Published: 10 February 2011
PDF: 15 pages
Proc. SPIE 7880, Media Watermarking, Security, and Forensics III, 78800P (10 February 2011); doi: 10.1117/12.871929
Show Author Affiliations
Christian Kraetzer, Otto-von-Guericke-Univ. Magdeburg (Germany)
Kun Qian, Otto-von-Guericke-Univ. Magdeburg (Germany)
Maik Schott, Otto-von-Guericke-Univ. Magdeburg (Germany)
Jana Dittmann, Otto-von-Guericke-Univ. Magdeburg (Germany)


Published in SPIE Proceedings Vol. 7880:
Media Watermarking, Security, and Forensics III
Nasir D. Memon; Jana Dittmann; Adnan M. Alattar; Edward J. Delp III, Editor(s)

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