Blind audio watermarking mechanism based on variational Bayesian learning - Xin TANG; Zhao-feng MA; Xin-xin NIU; Yi-xian YANG (Tongxin xuebao)
In order to improve the performance of audio watermarking detection,a blind audio watermarking mechanism using the statistical characteristics based on MFCC features of audio frames was proposed.The spread spectrum watermarking was embedded in the DCT coefficients of audio frames.MFCC features extracted from watermarked audio frames as well as un-watermarked ones were trained to establish their Gaussian mixture models and to estimate the parameters by vatiational Bayesian learning method respectively.The watermarking was detected according to the maximum likelihood principle.The experimental results show that our method can lower the false detection rate compared with the method using EM algorithm when the audio signal was under noise and malicious attacks.Also,the experiments show that the proposed method achieves better performance in handling insufficient training data as well as getting rid of over-fitting problem.