TY - GEN
T1 - Speech enhancement based on FLANN using both bone- and air-conducted measurements
AU - Huang, Boyan
AU - Xiao, Yegui
AU - Sun, Jinwei
AU - Wei, Guo
AU - Wei, Hongyun
N1 - Publisher Copyright:
© 2014 Asia-Pacific Signal and Information Processing Ass.
PY - 2014/2/12
Y1 - 2014/2/12
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84949925318
U2 - 10.1109/APSIPA.2014.7041679
DO - 10.1109/APSIPA.2014.7041679
M3 - 会议稿件
AN - SCOPUS:84949925318
T3 - 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014
BT - 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014
Y2 - 9 December 2014 through 12 December 2014
ER -