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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