Development and testing of an audio forensic software for enhancing speech signals masked by loud music
Robert Alexandru Dobre, Cristian Negrescu, Dumitru Stanomir · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
In many situations audio recordings can decide the fate of a trial when accepted as evidence. But until they can be taken into account they must be authenticated at first, but also the quality of the targeted content (speech in most cases) must be good enough to remove any doubt. In this scope two main directions of multimedia forensics come into play: content authentication and noise reduction. This paper presents an application that is included in the latter. If someone would like to conceal their conversation, the easiest way to do it would be to turn loud the nearest audio system. In this situation, if a microphone was placed close by, the recorded signal would be apparently useless because the speech signal would be masked by the loud music signal. The paper proposes an adaptive filters based solution to remove the musical content from a previously described signal mixture in order to recover the masked vocal signal. Two adaptive filtering algorithms were tested in the proposed solution: the Normalised Least Mean Squares (NLMS) and Recursive Least Squares (RLS). Their performances in the described situation were evaluated using Simulink, compared and included in the paper.