Speech enhancement based on FLANN using both bone- and air-conducted measurements

Bone conduction has been used for speech enhancement in very noisy environments. It usually includes nonlinearity in transmission, small non-stationary noise due to body movements (friction, collision, wind noise, crosstalk and so on), serious attenuation of high frequency components, etc. These problems usually lead to poor intelligibility of bone-conducted (BC) speech. Recoverying an air-conducted (AC) speech from a bone-conducted recording alone has been considered to be a very difficult task. In this paper, we propose a nonlinear adaptive noise canceller (ANC) that uses both bone- and air-conducted measurements for speech recovery. In this ANC, bone-conducted measurement is used as the reference signal while the air-conducted one is adopted as the primary signal, and a functional link artificial neural network (FLANN) is introduced as the nonlinear adaptive filter. Application to real speech signals is conducted to confirm the effectiveness of the proposed system.