摘要
提出一种基于BP神经网络和信噪比(SNR)的自适应音频水印算法。通过修改音频分抽样后子音频帧小波变换近似分量均值的方法嵌入水印。水印嵌入强度由期望的SNR值确定,可避免反复实验或者由经验数据确定水印强度。水印的提取以子音频抽样帧小波变换近似分量均值为BP神经网络的输入,以神经网络的输出作为提取的水印。经过模板训练的BP神经网络能有效的恢复嵌入到音频中的水印数据,实现了水印的盲检测。实验结果表明,本文提出的水印算法具有较好的鲁棒性和不可听性。
An adaptive audio watermarking algorithm based on BP neural networks and signal-to-noise ratio （SNR） is proposed. The watermark is embedded by modifying wavelet transform approximate coefficient mean of sub-sampling of audio frame. The watermarking strength is assured according to expected SNR and it can avoid using over and over experiments or experience data to confirm the watermarking strength. Mean of approximate coefficients of sub-sampling of audio frame is served as input of BP neural networks in process of watermark extracting. The output of neural networks is served as the extracted watermark. BP neural networks trained by template information can effectively restore the watermark embedded into audio, which realizes blind detection of watermark. The experimental results show that the proposed algorithm is robust and imperceptible.
出处
《光电子．激光》
EI
CAS
CSCD
北大核心
2012年第12期2391-2397,共7页
Journal of Optoelectronics·Laser
基金
国家自然科学基金(60901002)
广西教育厅立项课题(201010LX115)资助项目
关键词
音频水印
信噪比(SNR)
BP神经网络
audio watermarking
signal-to-noise ratio （SNR）
BP neural networks
作者简介
E-mail：494777@163.com.黄雄华（1971-），男，湖北荆州人，博士研究生，高级实验师，主要研究方向为数字水印．
