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

• 论文 • 上一篇    下一篇

基于设备噪声估计的录音设备源识别

邹领, 贺前华, 邝细超, 李洪滔, 蔡梓文   

  1. 华南理工大学 电子与信息学院,广州 510641
  • 收稿日期:2015-09-03 出版日期:2017-01-20 发布日期:2017-01-20
  • 通讯作者: 贺前华(1965-),男,教授,博士生导师.研究方向:语音及音频信号处理,声纹身份认证系统的安全.E-mail:eeqhhe@scut.edu.cn
  • 作者简介:邹领(1981-),男,博士研究生.研究方向:数字多媒体取证,语音及音频信号处理.E-mail:zou.ling@mail.scut.edu.cn
  • 基金资助:
    国家自然科学基金项目(61571192); 广东省公益项目(2015A010103003).

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

摘要: 针对录音设备源识别问题,首先分析了语音录音的产生过程,在此基础上提出了一种基于设备噪声估计的录音设备指纹,为了获取充分的设备噪声,使用了一个包含两种噪声估计算法的设备噪声估计器。为了验证提出的设备指纹的有效性,同时考虑了5种分类方法来进行录音设备源识别,并在两个录音库上进行了实验。其中一个包含22个录音设备,该22个设备分别来自4类常用的录音设备,另一个音库包含21个手机的语音录音。实验结果证明了本文方法的有效性。

关键词: 信息处理技术, 数字音频取证, 录音设备源识别, 噪声估计

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

中图分类号: 

  • 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.
[1] 苏寒松,代志涛,刘高华,张倩芳. 结合吸收Markov链和流行排序的显著性区域检测[J]. 吉林大学学报(工学版), 2018, 48(6): 1887-1894.
[2] 徐岩,孙美双. 基于卷积神经网络的水下图像增强方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1895-1903.
[3] 黄勇,杨德运,乔赛,慕振国. 高分辨合成孔径雷达图像的耦合传统恒虚警目标检测[J]. 吉林大学学报(工学版), 2018, 48(6): 1904-1909.
[4] 李居朋,张祖成,李墨羽,缪德芳. 基于Kalman滤波的电容屏触控轨迹平滑算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1910-1916.
[5] 应欢,刘松华,唐博文,韩丽芳,周亮. 基于自适应释放策略的低开销确定性重放方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1917-1924.
[6] 陆智俊,钟超,吴敬玉. 星载合成孔径雷达图像小特征的准确分割方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1925-1930.
[7] 刘仲民,王阳,李战明,胡文瑾. 基于简单线性迭代聚类和快速最近邻区域合并的图像分割算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1931-1937.
[8] 单泽彪,刘小松,史红伟,王春阳,石要武. 动态压缩感知波达方向跟踪算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1938-1944.
[9] 姚海洋, 王海燕, 张之琛, 申晓红. 双Duffing振子逆向联合信号检测模型[J]. 吉林大学学报(工学版), 2018, 48(4): 1282-1290.
[10] 全薇, 郝晓明, 孙雅东, 柏葆华, 王禹亭. 基于实际眼结构的个性化投影式头盔物镜研制[J]. 吉林大学学报(工学版), 2018, 48(4): 1291-1297.
[11] 陈绵书, 苏越, 桑爱军, 李培鹏. 基于空间矢量模型的图像分类方法[J]. 吉林大学学报(工学版), 2018, 48(3): 943-951.
[12] 陈涛, 崔岳寒, 郭立民. 适用于单快拍的多重信号分类改进算法[J]. 吉林大学学报(工学版), 2018, 48(3): 952-956.
[13] 孟广伟, 李荣佳, 王欣, 周立明, 顾帅. 压电双材料界面裂纹的强度因子分析[J]. 吉林大学学报(工学版), 2018, 48(2): 500-506.
[14] 林金花, 王延杰, 孙宏海. 改进的自适应特征细分方法及其对Catmull-Clark曲面的实时绘制[J]. 吉林大学学报(工学版), 2018, 48(2): 625-632.
[15] 王柯, 刘富, 康冰, 霍彤彤, 周求湛. 基于沙蝎定位猎物的仿生震源定位方法[J]. 吉林大学学报(工学版), 2018, 48(2): 633-639.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!