A Fast Audio Digital Watermark Method Based on Counter-Propagation Neural Networks

In this thesis, we present a novel audio digital watermark method based on counter-propagation Neural Networks. After dealing with the audio by discrete wavelet transform, we select the important coefficients which are ready to be trained in the neural networks. By making use of the capabilities of memorization and fault tolerance in CPN, watermark is memorized in the nerve cells of CPN. In addition, we adopt a kind of architecture with a adaptive number of parallel CPN to treat with each audio frame and the corresponding watermark bit. Comparing with other traditional methods by using CPN, it was largely improve the efficiency for watermark embedding and correctness for extracting, namely the speed of whole algorithm. The extensive experimental results show that, we can detect the watermark exactly under most of attacks. This method efficaciously tradeoff both the robustness and inaudibility of the audio digital watermark.