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L. Cuccovillo, S. Mann, P. Aichroth, M. Tagliasacchi, and C. Dittmar, "Blind Microphone Analysis and Stable Tone Phase Analysis for Audio Tampering Detection," Paper 8966, (2013 October.). doi: L. Cuccovillo, S. Mann, P. Aichroth, M. Tagliasacchi, and C. Dittmar, "Blind Microphone Analysis and Stable Tone Phase Analysis for Audio Tampering Detection," Paper 8966, (2013 October.). doi:
Abstract: In this paper we present an audio tampering detection method based on the combination of blind microphone analysis and phase analysis of stable tones, e.g., the electrical network frequency (ENF). The proposed algorithm uses phase analysis to detect segments that might have been tampered. Afterwards, the segments are further analyzed using a feature vector able to discriminate among different microphone types. Using this combined approach, it is possible to achieve a significantly lower false-positive rate and higher reliability as compared to standalone phase analysis.

@article{cuccovillo2013blind,
author={cuccovillo, luca and mann, sebastian and aichroth, patrick and tagliasacchi, marco and dittmar, christian},
journal={journal of the audio engineering society},
title={blind microphone analysis and stable tone phase analysis for audio tampering detection},
year={2013},
volume={},
number={},
pages={},
doi={},
month={october},} @article{cuccovillo2013blind,
author={cuccovillo, luca and mann, sebastian and aichroth, patrick and tagliasacchi, marco and dittmar, christian},
journal={journal of the audio engineering society},
title={blind microphone analysis and stable tone phase analysis for audio tampering detection},
year={2013},
volume={},
number={},
pages={},
doi={},
month={october},
abstract={in this paper we present an audio tampering detection method based on the combination of blind microphone analysis and phase analysis of stable tones, e.g., the electrical network frequency (enf). the proposed algorithm uses phase analysis to detect segments that might have been tampered. afterwards, the segments are further analyzed using a feature vector able to discriminate among different microphone types. using this combined approach, it is possible to achieve a significantly lower false-positive rate and higher reliability as compared to standalone phase analysis.},}

TY - paper
TI - Blind Microphone Analysis and Stable Tone Phase Analysis for Audio Tampering Detection
SP - EP -
AU - Cuccovillo, Luca
AU - Mann, Sebastian
AU - Aichroth, Patrick
AU - Tagliasacchi, Marco
AU - Dittmar, Christian
PY - 2013
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2013 TY - paper
TI - Blind Microphone Analysis and Stable Tone Phase Analysis for Audio Tampering Detection
SP - EP -
AU - Cuccovillo, Luca
AU - Mann, Sebastian
AU - Aichroth, Patrick
AU - Tagliasacchi, Marco
AU - Dittmar, Christian
PY - 2013
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2013
AB - In this paper we present an audio tampering detection method based on the combination of blind microphone analysis and phase analysis of stable tones, e.g., the electrical network frequency (ENF). The proposed algorithm uses phase analysis to detect segments that might have been tampered. Afterwards, the segments are further analyzed using a feature vector able to discriminate among different microphone types. Using this combined approach, it is possible to achieve a significantly lower false-positive rate and higher reliability as compared to standalone phase analysis.