법음성학에서의 오디오 신호의 위변조 구간 자동 검출 방법 연구
An Automatic Method of Detecting Audio Signal Tampering in Forensic Phonetics
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참고문헌
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초록
We propose a novel scheme for digital audio authentication of given audio files which are edited by inserting small audiosegments from different environmental sources. The purpose of this research is to detect inserted sections from given audio files. We expect that the proposed method will assist human investigators by notifying suspected audio section which considered to berecorded or transmitted on different environments. GMM-UBM and GSV-SVM are applied for modeling the dominantenvironment of a given audio file. Four kinds of likelihood ratio based scores and SVM score are used to measure thelikelihood for a dominant environment model. We also use an ensemble score which is a combination of the aforementioned fivekinds of scores. In the experimental results, the proposed method shows the lowest average equal error rate when we use theensemble score. Even when dominant environments were unknown, the proposed method gives a similar accuracy.
Abstract
We propose a novel scheme for digital audio authentication of given audio files which are edited by inserting small audiosegments from different environmental sources. The purpose of this research is to detect inserted sections from given audio files. We expect that the proposed method will assist human investigators by notifying suspected audio section which considered to berecorded or transmitted on different environments. GMM-UBM and GSV-SVM are applied for modeling the dominantenvironment of a given audio file. Four kinds of likelihood ratio based scores and SVM score are used to measure thelikelihood for a dominant environment model. We also use an ensemble score which is a combination of the aforementioned fivekinds of scores. In the experimental results, the proposed method shows the lowest average equal error rate when we use theensemble score. Even when dominant environments were unknown, the proposed method gives a similar accuracy.
참고문헌 (18)
- Brixen, E. B., "Techniques for the authentication of digital audio recording", Audio Engineering Society Convention 122, ~ (2007)
- Ojowu, O, "ENF extraction from digital recordings using adaptive techniques and frequency tracking", Forensics and Security, 7, pp.1330~1338 (2012)
- Grigoras, C., "Applications of ENF analysis in forensic authentication of digital audio and video recording", Journal of the Audio Engineering Society, 9, pp.643~661 (2009)
- Nicolade, D. P., "Evaluating digital audio authenticity with spectral distances and ENF phase change", proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.1417~1420 (2009)
- Malik, H, "Acoustic environment identification and its applications to audio forensics", IEEE Transactions on Information Forensics and Security, 8(11), pp.1827~1837 (2013)
- Kraetzer, C., "Digital audio forensics: a first practical evaluation on microphone and environment classification", proceedings of the 9th workshop on Multimedia & security, pp.63~74 (2007)
- Garcia-Romero, D., "Automatic acquisition device identification from speech recordings", proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing, pp.1806~1809 (2010)
- Luo, D., "Compression history identification for digital audio signal", proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.1733~1736 (2012)
- Jenner, F., "Highly accurate non-intrusive speech forensics for codec identifications from observed decoded signals", in proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.1737~1740 (2012)
- Reynolds, D. A., "Speaker Verification Using Adapted Gaussian Mixture Models", Digital Signal Processing, 10, pp.19~41 (2000)
- Campbell, W. M., "Support Vector Machines Using GMM Supervectors for Speaker Verification", IEEE Signal Processing Letters, 13(5), pp.308~311 (2006)
- Schölkopf, B., "Support Vector Method for Novelty Detection", Advances in Neural Information Processing Systems, 12, pp.582~588 (1999)
- 김명재, "화자 식별에서의 배경화자데이터를 이용한 히스토그램 등화 기법", 말소리와 음성과학, 4(2), pp.79~86 (2012), 한국음성학회[DOI]
- "FFmpeg"[URL]
- "LAME MP3 Encoder"[URL]
- "Opus Codec"[URL]
- Young, S., "The HTK Book(for HTK Version 3. 4)" (2009), Cambridge University Engineering Department
- "LIBSVM -- A Library for Support Vector Machines" (2014)[URL]