INFORMATION CHANGE THE WORLD

International Journal of Education and Management Engineering(IJEME)

ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)

Published By: MECS Press

IJEME Vol.2, No.1, Jan. 2012

An Adaptive Audio Watermarking Scheme Method Based on Kernel Fuzzy C-means Clustering

Full Text (PDF, 75KB), PP.73-80


Views:57   Downloads:1

Author(s)

Honghong Chen,Zulin Zhang

Index Terms

Audio signal;audio watermarking;adaptive watermarking;kernel fuzzy c-means clustering algorithm

Abstract

In this paper, we propose an adaptive audio watermarking scheme according to local audio features. Firstly, the original audio signal is partitioned into audio frames and these audio frames are transformed into DWT domain respectively. Next, the local features of each audio frame are extracted respectively, and these features are used to train kernel fuzzy c-means (KFCM) clustering algorithm. According to well-trained KFCM, the audio frames to embed the watermark are selected and their embedding strengths are determined adaptively. The experimental results show the proposed method is robust to common signal processing operations such as lossy compression (MP3), filtering, re-sampling, re-quantizing, etc.

Cite This Paper

Honghong Chen,Zulin Zhang,"An Adaptive Audio Watermarking Scheme Method Based on Kernel Fuzzy C-means Clustering", IJEME, vol.2, no.1, pp.73-80, 2012.

Reference

[1]H.J. Yang, J.C. Patra, C.W. Chan, “An artificial neural network-based scheme for robust watermarking of audio signals”, ICASSP'02, 1, 2002, pp.I-1029-1032.

[2]J. Wang, F.Z. Lin, “Digital audio watermarking based on support vector machine”. Journal of Computer Research and Development, 2005 Vol.42, No.9, pp.1605-1611. (in Chinese)

[3]S. Kirbiz, B. Gunsel, “Robust audio watermark decoding by supervised learning”, Proceedings of ICASSP 2006, 5, 2006, pp.V-761- V-764.

[4]X.-J. Xu, H. Peng, C.-Y. He, “DWT-based audio watermarking using support vector regression and subsampling”, In F.Masulli, S.Mitra, and G.Pasi (Eds.): WILF2007, LNAI 4578, 2007, pp. 136-144.

[5]H. Peng, X. Wang, W.X. Wang, J. Wang, D.Y. Hu, “Audio watermarking approach based on audio features in multiwavelet domain”, Journal of Computer Research and Development, 2010, 47(2), pp.216-222. (in Chinese)

[6]D.-W. Kima, K.Y. Leeb, D. Leea, K.H. Leea, “Evaluation of the performance of clustering algorithms in kernel-induced feature space”, Pattern Recognition, 38, 2005, pp.607-611.

[7]J.W. Liu, M.Z. Xu, “Kernelized fuzzy attribute c-means clustering algorithm”, Fuzzy Sets and Systems, 159, 2008, pp.2428-2445.

[8]http://www.petitcolas.net/fabien/steganography/mp3stego/index.html.

[9]S.Q. Wu, J.W.Huang, Y.Q. Shi, “Efficiently self-synchronized audio watermarking for assured audio data transmission”, IEEE Trans. on Broadcast, 2005, Vol 51, No.1 pp. 69-76.