000045220 000__ 02418cam\a22003615i\4500
000045220 001__ 45220
000045220 005__ 20220308120225.0
000045220 006__ m\\\\eo\\d\\\\\\\\
000045220 008__ 220308s2022\\\\sz\\\\\\\\\\\\000\0\eng\d
000045220 0247_ $$2doi$$a10.1109/ACCESS.2022.3145950
000045220 040__ $$aSzGeWIPO$$beng$$erda$$cSzGeWIPO$$dCaBNVSL
000045220 041__ $$aeng
000045220 24500 $$aAudio Watermarking for Security and Non-Security Applications /
000045220 264_1 $$aUnited States:$$b[IEEE],$$c2022
000045220 300__ $$a1 volume
000045220 337__ $$aunmediated$$bn$$2rdamedia
000045220 4901_ $$aIEEE Access ;$$vVolume 10, pp. 12654-12677, 2022.
000045220 500__ $$aISSN:  2169-3536
000045220 500__ $$aThis resource was extracted from the Directory of Open Access Journals (DOAJ)
000045220 520__ $$aThe digitization of audiovisual data is significantly increasing. Thus, to guarantee the protection of the intellectual properties of this digital content, watermarking has appeared as a solution. Watermarking can be used in reality in several types of applications that target two different contexts: the first for security applications and the second for non-security ones. In this paper, we carry a big interest in studying these two types of applications. Moreover, we propose a first digital watermarking scheme for security copyright protection applications, where we have involved neural network architecture in the insertion and detection processes, and integrated some masking phenomena of the human psychoacoustic model with linear predictive coding spectral envelope estimation of the audio file. Experiments proved the efficiency of exploiting perceptual masking with spectral envelope consideration in terms of imperceptibility and robustness results. In addition, we suggest a second audio watermarking technique for non-security content characterization applications based on a deep learning classification architecture. In this scheme, the extracted watermark advises about the audio class: music or speech, speaker gender, and emotion. The reported results indicated that the suggested scheme achieved a higher performance at the classification level, as well as at the watermarking properties.
000045220 588__ $$aCrossref
000045220 590__ $$aPublished online: 24- Jan-22
000045220 650_0 $$aTechnology.
000045220 650_0 $$aElectrical Engineering.
000045220 650_0 $$aNuclear Engineering.
000045220 650_0 $$aCopyright.
000045220 7001_ $$aCharfeddine, Maha,$$eauthor.
000045220 7001_ $$aMezghani, Eya,$$eauthor.
000045220 7001_ $$aMasmoudi, Salma,$$eauthor.
000045220 7001_ $$aAmar,  Chokri Ben,$$eauthor.
000045220 7001_ $$aAlhumyani, Hesham,$$eauthor.
000045220 7730_ $$tIEEE Access
000045220 830_0 $$aIEEE Access ;$$vVolume 10, pp. 12654-12677, 2022.
000045220 85641 $$uhttps://doi.org/10.1109/ACCESS.2022.3145950$$yOnline version
000045220 904__ $$aArticle
000045220 980__ $$aBIB