The development of computer and telecommunication technologies led to a
revolution in the way that people work and communicate with each other. One
of the results is that large amount of information will increasingly be held in a
form that is natural for users, as speech in natural language. In the presented
work, we investigate the speech signal capture problem, which includes the
separation of multiple interfering speakers using microphone arrays.
Adaptive beamforming is a classical approach which has been developed
since the seventies. However it requires a double-talk detector (DTD) that
interrupts the adaptation when the target is active, since otherwise target
cancelation occurs. The fact that several speakers may be active simultaneously
makes this detection difficult, and if additional background noise occurs,
even less reliable. Our proposed approaches address this separation problem
using continuous, uninterrupted adaptive algorithms. The advantage seems
twofold: Firstly, the algorithm development is much simpler since no detection
mechanism needs to be designed and no threshold is to be tuned. Secondly,
the performance may be improved due to the adaptation during periods of
double-talk.