JRM Vol.27 No.5 pp. 520-527
doi: 10.20965/jrm.2015.p0520


A Hybrid Nonlinear ANC for Speech Recovery Using Both Bone- and Air-Conducted Measurements

Ran Xiao*1, Yaping Ma*2, Boyan Huang*2, Yegui Xiao*3, and Koji Hasegawa*4

*1NEC Solution Innovators, Ltd.
1-40-1 Tomominami, Asaminami-ku, Hiroshima 731-3168, Japan

*2Harbin Institute of Technology
Box 351, Harbin 150001, China

*3Prefectural University of Hiroshima
1-1-71 Ujina-Higashi, Minami-ku, Hiroshima 734-8558, Japan

*4Hiroshima Prefectural Technology Research Institute
10-52 Motomachi, Naka-ku, Hiroshima 730-8511, Japan

April 29, 2015
August 6, 2015
October 20, 2015
speech recovery, adaptive noise canceller, bone-conducted speech, Volterra filter, functional link artificial neural network
Denoising nonlinear filter

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.

Cite this article as:
R. Xiao, Y. Ma, B. Huang, Y. Xiao, and K. Hasegawa, “A Hybrid Nonlinear ANC for Speech Recovery Using Both Bone- and Air-Conducted Measurements,” J. Robot. Mechatron., Vol.27, No.5, pp. 520-527, 2015.
Data files:
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Last updated on Nov. 15, 2018