LAUSR

The digitization of audiovisual data is significantly increasing. Thus, in order to guarantee principally the protection of intellectual properties of this digital content, watermarking has appeared as a solution. The watermarking can be used in reality in several types of applications that target two different contexts; the first for security applications and the second for non-security ones. In this paper, we carry a big interest in studying these two types of applications. Moreover, we propose a first digital watermarking scheme for security copyright protection application where we have involved Neural Network architecture in the insertion and detection processes and we have integrated some masking phenomena of the Human Psychoacoustic Model with Linear Predictive Coding spectral envelope estimation of the audio file. Experimentations proved the efficiency of exploiting perceptual masking with spectral envelope consideration in terms of imperceptibility and robustness results. Besides, we suggest a second audio watermarking technique for non-security content characterization application based on deep learning classification architecture. In this scheme, extracted watermark will advise about the audio class: music or speech, the speaker gender and emotion. Reported results indicated that the suggested scheme achieved higher performance at classification level as well as at watermarking properties.