Lei, Baiying, Song, Insu, and Rahman, Atiqur Shah (2012) Optimal watermarking scheme for breath sound. In: Proceedings of the 2012 International Joint Conference on Neural Networks. pp. 1-6. From: 2012 International Joint Conference on Neural Networks (IJCNN), 10-15 June 2012, Brisbane, QLD, Australia.

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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.

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