![]() Cross Platform on Android, iOS, macOS, tvOS, Linux and Windows. Fully featured for interactive audio for Games, VOIP, DAWs, Music Apps, Hi-Res Audio, Voice Assistants, Virtual, Augmented and Mixed Reality use-cases. The results lead to higher accuracy for forgery detection with the combination of ENF and phase feature.Īccuracy Audio Battery-powered devices ENF Forgery Phase.Ĭopyright © 2016 Elsevier Ireland Ltd. Superpowered Audio Platform for Desktop, Mobile, IoT and Embedded Applications. From experiment conducted, ENF alone give 50% and 60% accuracy for forgery detection in mobile phone and laptop respectively, while the proposed method shows 88% and 92% accuracy respectively, for forgery detection in battery-powered devices. To solve the ENF problem in terms of accuracy in battery-powered devices, a combination method of ENF and phase feature is proposed. ![]() In spite of suitable accuracy of ENF in a majority of plug-in powered devices, the weak accuracy of ENF in audio forgery detection for battery-powered devices, especially in laptop and mobile phone, can be consider as one of the main obstacles of the ENF. There are a number of methods for forgery detection, which electric network frequency (ENF) is one of the powerful methods in this area for forgery detection in terms of accuracy. In the last decade, there has been increasing attention to the audio forgery detection due to a significant increase in the number of forge in different type of audio. development of robust location, detection, diagnostic, identification. ![]() Audio forgery is any act of tampering, illegal copy and fake quality in the audio in a criminal way. radio frequency engineering applied mathematics.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |