Abstract
<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270005/08.jpg"" width=""300"" /> Denoising nonlinear filter</div> Speech recovery in the presence of very harsh noise is calling for R&D that take approaches different from those established to date, as the conventional systems and algorithms for speech denoising may suffer from serious performance degradation. In this paper, we propose a hybrid nonlinear adaptive noise canceller (ANC) to perform the speech enhancement task using both bone- and air-conducted measurements. In the proposed ANC, the bone-conducted speech serves as a reference signal while the air-conducted measurement with very large additive noise is adopted as a primary noise. A Volterra filter and a functional link artificial neural network (FLANN) are placed in parallel, forming a hybrid nonlinear ANC. Simulations using real bone- and air-conducted speech measurements are provided to demonstrate that the proposed system outperforms ANCs equipped with FIR filter or Volterra filter or FLANN alone. </span>
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