Home   >   CSC-OpenAccess Library   >    Manuscript Information
Quality and Distortion Evaluation of Audio Signal by Spectrum
Er. Niranjan Singh, Dr. Bhupendra Verma
Pages - 103 - 110     |    Revised - 15-01-2012     |    Published - 21-02-2012
Volume - 6   Issue - 1    |    Publication Date - February 2012  Table of Contents
component Steganalysis, watermarking, audio quality measures, feature selection, distortion metric
Information hiding in digital audio can be used for such diverse applications as proof of ownership, authentication, integrity, secret communication, broadcast monitoring and event annotation. To achieve secure and undetectable communication, stegano-objects, and documents containing a secret message, should be indistinguishable from cover-objects, and show that documents not containing any secret message. In this respect, Steganalysis is the set of techniques that aim to distinguish between cover-objects and stegano-objects [1]. A cover audio object can be converted into a stegano-audio object via steganographic methods. In this paper we present statistical method to detect the presence of hidden messages in audio signals. The basic idea is that, the distribution of various statistical distance measures, calculated on cover audio signals and on stegano-audio signals vis-à-vis their de-noised versions, are statistically different. A distortion metric based on Signal spectrum was designed specifically to detect modifications and additions to audio media. We used the Signal spectrum to measure the distortion. The distortion measurement was obtained at various wavelet decomposition levels from which we derived high-order statistics as features for a classifier to determine the presence of hidden information in an audio signal. This paper looking at evidence in a criminal case probably has no reason to alter any evidence files. However, it is part of an ongoing terrorist surveillance might well want to disrupt the hidden information, even if it cannot be recovered
CITED BY (1)  
1 Jain, P., Trivedi, V. K., & LNCT, B. (2012). A Novel Technique for Data Hiding in Audio by Using DWTs. International Journal of Computational Engineering and Management, 15(4), 2230-7893.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Bielefeld Academic Search Engine (BASE) 
5 Scribd 
6 SlideShare 
7 PdfSR 
Bassia, P. and I. Pitas, “Robust Audio Watermarking in the Time Domain”, in 9th European Signal Processing Conference (EUSIPCO’98), Island of Rhodes, Greece, 8–11 Sept. 1998.
Beerends, J. G. and J. A. Stemerdink, “A Perceptual Audio Quality Measure Based on a Psychoacoustics Sound Representation,” J. Audio Eng. Soc., Vol. 40, pp.63- 978, Dec. 1992.
Bender, W., D. Gruhl, N. Morimoto, and A. Lu, “Techniques for data hiding”, IBM Systems Journal, vol. 35, no: 3&4, pp. 313-336, 1996.
Chen, B. and G. W. Wornell, “Quantization Index Modulation: a Class of Probably Good Methods for Digital Watermarking and Information Embedding”, IEEE Trans. on Information Theory, Vol. 47, No. 4, pp. 1423-1443, May 2001.
Coifman, R. R., and D. L. Donoho, “Translation-invariant denoising,” in Wavelets and Statistics A. Antoniadis and G. Oppenheim, Eds, Springer-Verlag lecture notes, San Diego, 1995.
Cox, I., J. Kilian, F. T. Leighton, and T. Shamoon, “Secure Spread Spectrum Watermarking for Multimedia”, IEEE Trans. on Image Process., Vol. 6, No. 12, pp. 1673-1687, Dec 1997.
Er. Niranjan Singh and Dr. Bhupendra Verma, “Steganalysis of Audio Signals, Audio Quality and Distortion Measures” ICCET 2010 - International Conference on Computer Engineering and Technology CET6011.0.607 ISBN No 978-81-920748-1-8.
Johnson, N.F., S. Jajodia, “Steganalysis of images created using current steganography software”, in David Aucsmith (Ed.): Information Hiding, LNCS 1525, pp. 32-47. Springer- Verlag Berlin Heidelberg, 1998.
Kitawaki, N., H. Nagabuchi, and K. Itoh, “Objective quality evaluation for low-bit-rate speech coding systems,” IEEE J. Select. Areas Commun., vol. 6, pp. 242-248, Feb. 1988.
Stirmark,http://amslsmb.cs.unimagdeburg.de/smfa/main.asp, 2004.
Swanson, M. D., Bin Zhu, Ahmed H. Tewfik, and Laurence Boney, “Robust Audio Watermarking Using Perceptual Masking”, Signal Processing 66, pp. 337-355, 1998.
Voloshynovsky, S., S. Pereira, V. Iquise, and T. Pun, “Attack modeling: towards a second generation watermarking benchmark”, Signal Processing, vol. 81, pp. 1177-1214, 2001.
Voran, S., “Objective estimation of perceived speech quality, part I: development of the measuring normalizing block technique”, IEEE Transactions on Speech and Audio Processing, in Press, 1999.
Wang, S., A. Sekey, and A. Gersho, “An objective measure for predicting subjective quality of speech coders”, IEEE J. Select. Areas Commun., vol. 10, pp. 819-829, June 1992.
Westfeld, A. Pfitzmann, “Attacks on steganographic systems”, in Information Hiding, LNCS 1768, pp. 61-66, Springer-Verlag Heidelberg, 1999.
Yang, W, M. Dixon, and R. Yantorno, “A modified bark spectral distortion measure which uses noise masking threshold,” IEEE Speech Coding Workshop, pp. 55-56, Pocono Manor, 1997.
Mr. Er. Niranjan Singh
- India
Mr. Dr. Bhupendra Verma
- India

View all special issues >>