A blind audio watermarking scheme based on neural network and psychoacoustic model with error correcting code in wavelet domain

Abstract

Audio watermarking is a method that embeds inaudible information into digital audio data. This paper proposes an audio watermarking technique for protecting audio copyrights based on human psychoacoustic model (HPM), discrete wavelet transform (DWT), neural network (NN) and error correcting code. Our technique exploits frequency perceptual masking studied in HPM to guarantee that the embedded watermark is inaudible. To assure watermark embedding and extraction, neural network is used to memorize the relationships between a Wavelet central sample and its neighbors. To increase robustness of the scheme, the watermark is refined by the Hamming error correcting code while the encoded mark is embedded as new watermark in the transformed audio signal. Our audio watermarking algorithm is robust to common audio signal manipulations like MP3 compression, noise addition, silence addition, bit per sample conversion, noise reduction, dynamic changes and Notch filtering. Furthermore, it allows blind retrieval of embedded watermark which does not need the original audio and makes the watermark perceptually inaudible.