A DIRECTIONAL ADAPTIVE LEAST-MEAN-SQUARE ACOUSTIC ARRAY FOR HEARING-AID ENHANCEMENT

被引:6
|
作者
DEBRUNNER, VE
MCKINNEY, ED
机构
[1] The School of Electrical Engineering, The University of Oklahoma, Norman, Oklahoma 73019-0631
来源
关键词
D O I
10.1121/1.414360
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
This paper introduces directional microphones to adaptive array processing for hearing aid applications. Acoustic fixed arrays are designed to match a focused array gain pattern, while acoustic adaptive arrays are designed to attenuate interference noises with changing characteristics. However, as currently constructed, acoustic adaptive arrays cannot stay focused with a limited number of microphones available in a cosmetically acceptable hearing aid. In this paper, a technique is discussed that combines fixed and adaptive arrays in a system which enhances the desired signal while effectively attenuating interference speech and background noise. In particular, the design and performance of a directional adaptive least-mean-square (LMS) acoustic four-element array with a restricted geometry, where the array microphones are directional microphones, are examined. Simulations show that the directivity index of the directional adaptive array using four hypercardioid microphones is improved to between 8.6 and 11 dB. The array reduces the interference noises by 29.7 to 42.3 dB and provides a signal-to-noise ratio improvement of 11.5 to 12.2 dB over a single omnidirectional microphone. The sensitivity analysis is also discussed. It is concluded that the small size (four-element) microphone array can spatially filter interference noise effectively and so improve SNR performance significantly. © 1995, Acoustical Society of America. All rights reserved.
引用
收藏
页码:437 / 444
页数:8
相关论文
共 50 条
  • [21] A fast convergence normalized least-mean-square type algorithm for adaptive filtering
    Benallal, A.
    Arezki, M.
    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2014, 28 (10) : 1073 - 1080
  • [22] Adaptive multitask clustering algorithm based on distributed diffusion least-mean-square estimation
    Hua, Yi
    Wan, Fangyi
    Liao, Bin
    Zong, Yipeng
    Zhu, Shenrui
    Qing, Xinlin
    INFORMATION SCIENCES, 2022, 606 : 628 - 648
  • [23] A Reduced Gaussian Kernel Least-Mean-Square Algorithm for Nonlinear Adaptive Signal Processing
    Yuqi Liu
    Chao Sun
    Shouda Jiang
    Circuits, Systems, and Signal Processing, 2019, 38 : 371 - 394
  • [24] LEAST-MEAN-SQUARE SPATIAL FILTER FOR IR SENSORS
    TAKKEN, EH
    FRIEDMAN, D
    MILTON, AF
    NITZBERG, R
    APPLIED OPTICS, 1979, 18 (24): : 4210 - 4222
  • [25] A Reduced Gaussian Kernel Least-Mean-Square Algorithm for Nonlinear Adaptive Signal Processing
    Liu, Yuqi
    Sun, Chao
    Jiang, Shouda
    CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2019, 38 (01) : 371 - 394
  • [26] The Generalized Complex Kernel Least-Mean-Square Algorithm
    Boloix-Tortosa, Rafael
    Jose Murillo-Fuentes, Juan
    Tsaftaris, Sotirios A.
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2019, 67 (20) : 5213 - 5222
  • [27] Distributed average consensus with least-mean-square deviation
    Xiao, Lin
    Boyd, Stephen
    Kim, Seung-Jean
    JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 2007, 67 (01) : 33 - 46
  • [28] FREQUENCY-DOMAIN LEAST-MEAN-SQUARE ALGORITHM
    NARAYAN, SS
    PETERSON, AM
    PROCEEDINGS OF THE IEEE, 1981, 69 (01) : 124 - 126
  • [29] A complementary least-mean-square algorithm of adaptive filtering for SQUID based magneto cardiography
    Li, Z
    Chen, GH
    Zhang, LH
    Yang, QS
    Feng, J
    CHINESE PHYSICS, 2005, 14 (06): : 1095 - 1100
  • [30] Normalised least-mean-square algorithm for adaptive filtering of impulsive measurement noises and noisy inputs
    Jung, Sang Mok
    Park, PooGyeon
    ELECTRONICS LETTERS, 2013, 49 (20) : 1270 - 1271