An adaptive recurrent neuro-fuzzy filter for noisy speech enhancement

Abstract

This work presents a novel adaptive recurrent neuro-fuzzy filter (ARNFF) for speech enhancement in noisy environment. The speech enhancement scheme consists of two microphones that receive a primary and a reference input source respectively, and the proposed ARNFF that attenuates the noise corrupting the original speech signal in the primary channel. The ARNFF is a connectionist network that can be translated effortlessly into a set of dynamic fuzzy rules and state-space equations as well. An effective learning algorithm, consisting of a clustering algorithm for the structure learning and a recurrent learning algorithm for the parameter learning, is adopted from our previous research for the ARNNF construction. From our computer simulations and comparisons with some existing filters, the advantages of the proposed ARNFF for noisy speech enhancement include: 1) a more compact filter structure, 2) no a priori knowledge needed for the exact lagged order of the input variables, 3) a better performance in long-delay environment.

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