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Extending a context model for microphone forensics

In this paper, we extend an existing context model for statistical pattern recognition based microphone forensics by: first, generating a generalized model for this process and second, using this general model to construct a complex new application scenario model for microphone forensic investigations on the detection of playback recordings (a.k.a. replays, re-recordings, double-recordings). Thereby, we build the theoretical basis for answering the question whether an audio recording was made to record a playback or natural sound. The results of our investigations on the research question of playback detection imply that it is possible with our approach on our evaluation set of six microphones. If the recorded sound is not modified prior to playback, we achieve in our tests 89.00% positive indications on the correct two microphones involved. If the sound is post-processed (here, by normalization) this figure decreases (in our normalization example to 36.00%, while another 50.67% of the tests still indicate two microphones, of which one has actually not been involved in the recording and playback recording process).