基于经验模式分解的音频水印算法

针对传统基于经验模式分解(EMD)的音频水印算法鲁棒性不强的问题,提出一种基于固有模态函数(IMF)极值的盲音频水印算法.首先对音频信号进行分帧,每个音频帧经过EMD后得到IMF; 接着利用均匀量化的方法将水印信息和同步码嵌入到最后一个IMF的极值中.所提算法的数据嵌入率是46.9~50.3 b/s,且携水印音频保持了原始音频的感知质量.对携水印音频进行加噪、MP3压缩、重新采样、滤波、剪切和重采样攻击后,提取出的水印信息变化不大,算法鲁棒性较好.与时间域和小波域算法相比,提出的算法在保证高数据嵌入率的同时,可以抵抗32 kb/s的MP3压缩攻击.

Audio watermarking scheme based on empirical mode decomposition

For the issue that the robustness of the traditional audio watermarking algorithm based on Empirical Mode Decomposition (EMD) is not strong, an blind audio watermarking algorithm based on the extremum of Intrinsic Mode Function (IMF) was presented. The original audio signal was segmented firstly, and the audio frame was decomposed to a series of IMFs by EMD. Watermarking bits and synchronization code were embedded in the extremum of the last IMF by mean quantization. The embedding payload of the proposed method was 46.9~50.3 b/s, and the watermarked audio signal keeps the perceptive quality of the original audio signal. Several signal attacks such as adding noise, MP3 compression, re-sampling, filtering and cropping were imposed on the watermarked audio. The extracted watermarking bit changed a little, which shows the robustness of the proposed scheme. Compared with time domain and wavelet domain methods, the proposed method can resist 32 kb/s MP3 compression attack with high embedding payload.