Adaptive Blind Audio Signal Separation algorithm

using a TI DSP Blind Signal Separation (BSS) deals with the problem of separating independent sources from their observed mixtures while both the mixing process and original sources are unknown. Examples of BSS algorithms employed in acoustical applications can be found among others in audio teleconferencing systems, hearing aids, noise cancelling etc. Such systems usually involve speakers that are at some unknown, time varying, distances from the microphones. Therefore the recorded microphone signals contain the unknown original sources mixed by unknown acoustical transfer functions. The main objective of such a system is to produce speech with a high-quality intelligibility despite a low signal-noise ratio of the observations. Above that, in contrast to many other techniques (e.g. beamforming) the blind approach is independent of the actual source locations. In this project such a high quality is achieved by using algorithms that operate across the entire audio spectrum. The complexity of the entire system is related to the room acoustics. The acoustic model of a room is very complex. In addition, due to the continuously changing nature of the acoustics, the (un)mixing system is time varying. Therefore, adaptive filters are required. Recently in our research group a new adaptive blind signal separation algorithm, which still is in research, has been introduced into literature [Yin]. This algorithm is based on a simplified mixing model and second order statistics. The simplified mixing model uses the property that the acoustic transfer functions from a source to two closely spaced microphones are very similar, therefore only a difference between these two functions is needed. The algorithm is efficiently realized in frequency domain. The main purpose of this project is to make a realization of this Adaptive Blind Audio Signa