The step gain of adaptive algorithms for acoustic echo cancellation should vary with time in dependence of the far-end signal, the local signal, and the adaptation quality. It can be described as state-dependent, which is also the case for the control parameters of other algorithms in a hands-free telephone set, such as adaptive loss control or noise reduction. Most of the known state detection methods can only distinguish between subsets of the states, and are unreliable for certain state transitions, so that they should be combined for improved reliability of the state detection. In this contribution, state detection is performed by a fuzzy learning vector quantization/self-organizing map approach to combine several state detectors. The step gain is inferred from state information using fuzzy logic concepts. Finally, adaptation results are compared to the best conventional step-gain control method used in the complete structure.
Read full abstract