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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Baiying Lei Insu Song Rahman, S.A. |
| Copyright Year | 2012 |
| Description | Author affiliation: James Cook University Australia, School of Business/IT, Singapore (Baiying Lei; Insu Song; Rahman, S.A.) |
| Abstract | In this paper, a new watermarking scheme for breath sound based on lifting wavelet transform (LWT), discrete cosine transform (DCT), singular value decomposition (SVD) and dither modulation (DM) quantization is proposed to embed encrypted source and identity information, and medical conditions, such as cold and flu symptoms in breath sound while preserving important biological signals for detecting breathing patterns and breathing rates. In the proposed scheme, LWT is first carried out to decompose the signal followed by applying DCT on the approximate coefficients. SVD is then performed on the LWT-DCT coefficients to get the singular values. The novelty of our proposed method includes the introduction of the particle swarm optimization (PSO) technique to optimization the quantization steps of the DM approach too. Simulation results show that our watermarking scheme achieves good robustness against common signal processing attacks and maintains the imperceptivity. The comparison results also show good performance of our scheme. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 996516 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467314886 |
| ISSN | 21614393 |
| e-ISBN | 9781467314909 |
| e-ISBN | 9781467314893 |
| DOI | 10.1109/IJCNN.2012.6252586 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-10 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Watermarking Robustness Quantization Signal to noise ratio Discrete cosine transforms Optimization breath sound digital watermarking robust LWT-DCT SVD PSO |
| Content Type | Text |
| Resource Type | Article |
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