No abstract

No abstract

No abstract

In order to protect the digital audio and video products copyright in the network, an improved audio blind watermarking algorithm scheme based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is proposed. In the algorithm, an original audio is split as blocks and each block is decomposed on discrete wavelet transform for two degree, then first quarter audio approximate sub-band coefficients are decomposed on SVD transform, obtain a diagonal matrix. The watermarking information is embedded into the diagonal matrix. Experiments display that the transparency of  the proposed algorithm is better, and robustness is strong against the popular audio signal attack such as resampling, low-pass filter, requantization, Gaussian white noise,  MP3 compression and popular audio signal attack method has stronger robustness, average normalized correlation coefficient NC > 0.950, average BER<0.048.

Evoked potentials (EPs) have been widely used to quantify neurological system properties. Traditional EP analyses are developed under the condition that the background noise in EP analysis are Gaussian distributed. Alpha stable distribution, a generalization of Gaussian, is better for modeling impulsive noise than Gaussian distribution in biomedical signal processing. Conventional blind separation and estimation method of evoked potentials is based on second order statistics (SOS). In this paper, we modify our conventional algorithms and analyze the stability and convergence performance s of the new algorithm. The simulation experiments show that the proposed algorithm based on fractional lower order statistics is more robust than the conventional algorithm based on second order statistics.