In the context of music production “Listening” is the main tool for judging the aesthetic quality of audio recordings. However, for our ears to correctly judge artistic and technical quality, they must “work” in controlled conditions. Overall, this paper puts emphasis on the main methods of simulating acoustical parameters for a given room, acquiring real-world data by measuring the room in question, underlining issues, possible solutions, and establishing the accuracy of the methods used. This research aims to design and develop a listening room for proper subjective assessment of release ready program materials, with the principal focus on further development of intelligent music production tools.
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.
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