Waveform audio (WAV) file is a widely used file format of uncompressed audio. With the rapid
development of digital media technology, one can easily insert duplicated segments with powerful audio editing
software, e.g. inserting a segment of audio with negative meaning into the existing audio file. The duplicated
segments can change the meaning of the audio file totally. So for a WAV file to be used as evidence in legal
proceedings and historical documents, it is very importance to identify if there are any duplicated segments in it.
This paper proposes a method to detect duplicated segments in a WAV file. Our method is based on the similarity
calculation between two different segments. Duplicated segments are prone to having similar audio waveform,
i.e., a high similarity. We use fast convolution algorithm to calculate the similarity, which makes our method
quit efficient. We calculate the similarity between any two different segments in a digital audio file and use the
similarity to judge which segments are duplicated. Experimental results show the feasibility and efficiency of our
method on detecting duplicated audio segments.
XIAO Ji-nian1 (肖佶年), JIA Yun-zhe1 (贾蕴哲), FU Er-dong1 (付尔东),HUANG Zheng1* (黄征), LI Yan2 (李岩), SHI Shao-pei2 (施少培)
. Audio Authenticity: Duplicated Audio Segment Detection in Waveform Audio File[J]. Journal of Shanghai Jiaotong University(Science), 2014
, 19(4)
: 392
-397
.
DOI: 10.1007/s12204-014-1515-5
[1] Farid H. Detecting digital forgeries using bispectral analysis [R]. Cambridge, USA: Perceptual Science Group, MIT, 1999.
[2] Cano P, Batle E, Kalker T, et al. A review of algorithms for audio fingerprinting [C]//Proceedings of 2002 IEEE Workshop on Multimedia Signal Processing.Piscataway, USA: IEEE, 2002: 169-173.
[3] Grigoras C. Digital audio recording analysis: The electric network frequency criterion [J]. International Journal of Speech Language and the Law, 2005, 12(1):63-76.
[4] Sinitsyn A. Duplicate song detection using audio fingerprinting for consumer electronics devices[C]//Proceedings of 2006 IEEE Tenth International Symposium on Consumer Electronics (ISCE’06). Piscataway,USA: IEEE, 2006: 1-6.
[5] Yao Qiu-ming, Chai Pei-qi, Xuan Guo-rong, et al.Audio re-samplingdetection in audio forensics based on EM algorithm [J]. Computer Applications, 2006,26(11): 2598-2601(in Chinese).
[6] Kraetzer C, Oermann A, Dittmann J, et al.Digital audio forensics: A first practical evaluation on microphone and environment classification[C]//Proceedings of the 9th Workshop on Multimedia and Security. New York, USA: ACM, 2007: 63-74.
[7] Yang R, Qu Z, Huang J. Detecting digital audio forgeries by checking frame offsets [C]//Proceedings of the 10th ACM Workshop on Multimedia and Security.New York, USA: ACM, 2008: 21-26.
[8] Maher R C. Audio forensic examination: Authenticity,enhancement, and interpretation [J]. IEEE Signal Processing Magazine, 2009, 26(2): 84-94.
[9] Maher R C. Overview of audio forensics [C]//Intelligent Multimedia Analysis for Security Applications. Berlin, Germany: Springer-Verlag, 2010:127-144.
[10] Rodr′guez D, Apolin′ario J, Biscainho L. Audio authenticity: Detecting ENF discontinuity with high precision phase analysis [J]. IEEE Transactions on Information Forensics and Security, 2010, 5(3): 534-543.
