Applied Mathematics & Information Sciences

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Previous researches on digital audio watermarking has shown that effective techniques ensure inaudibility, reliability, robustness and protection against signal degradation. Crucial to this is the appropriate position of the watermark in the files. There is a risk of perceivable distortion in the audio signal when the watermark is spread in the audio spectrum, which may result in the loss of the watermark. This paper addresses the lack of an optimal position for the watermark when spread spectrum watermarking techniques are used. In an attempt to solve this problem, we model various positions on the audio spectrum for embedding the watermark and use a neural network (feed forward neural network) to predict the best positions for the watermark in the host audio streams. We are able to determine optimal position. The result of the neural network experiment formulated within the spread spectrum watermarking technique enables us to determine the best position for embedding. After embedding, further experimental results on the strength of the watermarking technique utilizing the outcome of the neural network show a high level of robustness against a variety of signal degradations. The contribution of this work is to show that audio signals contain patterns which help determine the most appropriate points at which watermarks should be embedded.

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