Single-channel speech enhancement: Using recurrent neuro-fuzzy voice activity detector and spectral subtraction algorithms

This paper investigates the effectiveness of a single-channel speech enhancement system that contains spectral subtraction and voice activity detection algorithm for noise elimination. We first extract features from a noisy signal and use these features as the inputs of a recurrent neuro-fuzzy network for detecting the voice activities of the signal. Based on the detection, we describe the characteristics of the background noise of the speech segments by a minimum frequency energy (MFE) parameter and then apply spectral subtraction algorithms with the parameter to eliminate the noise. Our simulation results show that the proposed enhancement system with a nonlinear spectral subtraction algorithm has superior performance.

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