吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (1): 274-280.doi: 10.13229/j.cnki.jdxbgxb201701040

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Source recording device recognition based on device noise estimation

ZOU Ling, HE Qian-hua, KUANG Xi-chao, LI Hong-tao, CAI Zi-wen   

  1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China
  • Received:2015-09-03 Online:2017-01-20 Published:2017-01-20

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Abstract: To resolve this problem, first, the procedure of the speed recording is analyzed and a recording device fingerprint based on device noise is proposed. Then, a noise estimator including two noise estimation algorithms is applied to acquire sufficient device noise. Finally, to evaluate the effectiveness of the proposed device fingerprint, five classification techniques are utilized to identify the source recording device. Experiments are carried out on two corpora. One corpus comprises speech recordings obtained by 22 recording devices coming from four types of common recording devices, and the other corpus consists of speech recordings from 21 cell phones. Experiment results verify the effectiveness of the proposed method.

Key words: information processing, digital audio forensic, source recording device recognition, noise estimation

CLC Number: 

  • TN912.3
[1] Zhao H, Malik H. Audio recording location identification using acoustic environment signature[J]. IEEE Trans Inf Forensics Security, 2013, 8(11): 1746-1759.
[2] 曾锦华,施少培,杨旭,等. 数字录音真实性司法鉴定研究现状[J]. 中国司法鉴定, 2014(4): 57-61.
Zeng Jin-hua, Shi Shao-pei, Yang Xu, et al. The state of art in digital audio forensic authentication[J]. Chinese Journal of Forensic Sciences, 2014(4): 57-61.
[3] Kraetzer C, Oermann A, Dittmann J, et al. Digital audio forensics: a first practical evaluation on microphone and environment classification[C]∥The Workshop on Multimedia and Security, Dallas, TX, USA, 2007: 63-74.
[4] Kraetzer C, Schott M, Dittmann J. Unweighted fusion in microphone forensics using a decision tree and linear logistic regression models[C]∥The 11th ACM Multimedia and Security Workshop, New York, 2009: 49-56.
[5] Buchholz R, Kraetzer C, Dittmann J. Microphone Classification Using Fourier Coefficients[M].Berlin: Springer, 2010:235-246.
[6] Kraetzer C, Qian K, Schott M, et al. A context model for microphone forensics and its application in evaluations[C]∥SPIE Conference on Media Watermarking, Security, and Forensics, London,2011.
[7] Kraetzer C, Qian K, Dittmann J. Extending a context model for microphone forensics[C]∥Proc SPIE, 2012, 8303:265-298.
[8] Malik H, Miller J. Microphone identification using higher-order statistics[C]∥AES 46th Conf Audio Forensics, Denver, CO, USA,2012.
[9] Eskidere O. Source microphone identification from speech recordings based on a Gaussian mixture model[J]. Turkish Journal of Electrical Engineering and Computer Sciences, 2014, 22(3): 754-767.
[10] Garcia-Romero D, Espy-Wilson C. Speech forensics: automatic acquisition device identification[J]. J Acoust Soc Am, 2010, 127(3):2044.
[11] Garcia-Romero D, Espy-Wilson C. Automatic acquisition device identification from speech recordings[C]∥Proc IEEE Int Conf Acoustics, Speech, Signal Processing (ICASSP), Dallas, TX, USA, 2010: 1806-1809.
[12] Garcia-Romero D, Espy-Wilson C. Automatic acquisition device identification from speech recordings[J]. J Audio Eng Soc, 2009, 124(4): 2530.
[13] Reynolds D A. HTIMIT and LLHDB: speech corpora for the study of handset transducer effects[C]∥Proc IEEE Int Conf Acoustics, Speech, Signal Processing (ICASSP), Munich, Germany, 1997:1535-1538.
[14] Kishore S P, Yegnanarayanana B. Identification of handset type using autoassociative neural networks[C]∥Fourth Int Conf Advances in Pattern Recognition and Digital Techniques, 1999:353-356.
[15] Mak M W, Kung S Y. Combining stochastic feature transformation and handset identification for telephone-based speaker verification[C]∥Proc IEEE Int Conf Acoustics, Speech, Signal Processing (ICASSP),Orlando,FL,2002:701-704.
[16] Panagakis Y, Kotropoulos C. Automatic telephone handset identification by sparse representation of random spectral features[C]∥The 14th ACM Multimedia and Security Workshop, Coventry, UK,2012:91-96.
[17] Panagakis Y, Kotropoulos C. Telephone handset identification by feature selection and sparse representations[C]∥IEEE Int Workshop Information Forensics and Security, Tenerife, Spain, 2012:73-78.
[18] Kotropoulos C. Telephone handset identification using sparse representations of spectral feature sketches[C]∥First Int Workshop Biometrics and Forensics, Lisbon, Portugal, 2013:1-4.
[19] Kotropoulos C. Source phone identification using sketches of features[J]. IET Biometrics,2014,3(2):75-83.
[20] Kotropoulos C, Samaras S. Mobile phone identification using recorded speech signals[C]∥The 19th International Conference on Digital Signal Processing (DSP), Hongkong,2014:586-591.
[21] Hanilçi C, Ertas F, Ertas T, et al. Recognition of brand and models of cell-phones from recorded speech signals[J]. IEEE Trans Inf Forensics Security, 2012,7(2):625-634.
[22] Hanilçi C, Kinnunen T. Source cell-phone recognition from recorded speech using non-speech segments[J]. Digital Signal Processing, 2014,35:75-85.
[23] Jahanirad M, Wahab A W A, Anuar N B, et al. Blind source mobile device identification based on recorded call[J]. Engineering Applications of Artificial Intelligence, 2014,36:320-331.
[24] Zou L, Yang J C, Huang T S. Automatic cell phone recognition from speech recordings[C]∥IEEE China Summit & International Conference on Proc Signal and Information Processing (China SIP), Beijing,2014:621-625.
[25] Zou L, He Q H, Feng X H. Cell phone verification from speech recordings using sparse representation[C]∥Proc IEEE Int Conf Acoustics, Speech, Signal Processing (ICASSP), New York,2015:1787-1791.
[26] 曾锦华,施少培,杨旭,等. 录音设备识别司法鉴定技术研究[J]. 中国司法鉴定, 2014(6):22-25.
Zeng Jin-hua, Shi Shao-pei, Yang Xu, et al. Research on methods of audio recorder forensic identification[J]. Chinese Journal of Forensic Sciences, 2014(6):22-25.
[27] 贺前华, 王志锋, Rudnicky A I, 等. 基于改进PNCC特征和两步区分性训练的录音设备识别方法[J]. 电子学报, 2014,42(1):191-198.
He Qian-hua, Wang Zhi-feng, Rudnicky A I, et al. A recording device identification algorithm based on improved PNCC feature and two-step discriminative training[J]. Acta Electronica Sinica, 2014,42(1):191-198.
[28] Boll S F. Suppression of acoustic noise in speech using spectral subtraction[J]. IEEE Trans Acoust, Speech, Signal Process, 1979,27(2):113-120.
[29] Kamath S, Loizou P. A multi-band spectral subtraction method for enhancing speech corrupted by colored noise[C]∥Proc IEEE Int Conf Acoustics, Speech, Signal Processing (ICASSP), Orlando, FL, 2002: 4164-4167.
[30] Doblinger G. Computationally efficient speech enhancement by spectral minima tracking in subbands[C]∥EUROSPEECH, Berlin,Germany,1995:1513-1516.
[31] Duda R O, Hart P E, Stork D G. Pattern Classification[M]. 2nd ed. New York: Wiley, 2000.
[32] Hall M, Frank E, Holmes G, et al. The WEKA data mining software: an update[J]. SIGKDD Explorations Newsletter, 2009,11(1):10-18.
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