10 February 2011 A context model for microphone forensics and its application in evaluations
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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.
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Christian Kraetzer, Christian Kraetzer, Kun Qian, Kun Qian, Maik Schott, Maik Schott, Jana Dittmann, Jana Dittmann, } "A context model for microphone forensics and its application in evaluations", Proc. SPIE 7880, Media Watermarking, Security, and Forensics III, 78800P (10 February 2011); doi: 10.1117/12.871929; https://doi.org/10.1117/12.871929


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