Incorporating spatial information in binaural beamforming for noise suppression in hearing aids

In this paper, we propose a beamforming algorithm for binaural hearing aids with enhanced noise suppression capability. The enhancement is based on incorporating a priori spatial information into the conventional multichannel Wiener filtering (MWF) approach for noise suppression. We develop a low complexity algorithm for the resulting quadratically constrained beamforming problem. Through numerical experiments, we demonstrate that the new algorithm can achieve better noise suppression performance than the existing beamforming algorithms under fairly realistic conditions. In addition, we propose two techniques to further reduce the algorithm's computational complexity and the communication overhead between two hearing aids without sacrificing the noise suppression performance.

[1]  Jonathan G. Fiscus,et al.  Darpa Timit Acoustic-Phonetic Continuous Speech Corpus CD-ROM {TIMIT} | NIST , 1993 .

[2]  Marc Moonen,et al.  Reduced-Bandwidth and Distributed MWF-Based Noise Reduction Algorithms for Binaural Hearing Aids , 2009, IEEE Transactions on Audio, Speech, and Language Processing.

[3]  J. Capon High-resolution frequency-wavenumber spectrum analysis , 1969 .

[4]  Jacob Benesty,et al.  On Optimal Frequency-Domain Multichannel Linear Filtering for Noise Reduction , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[5]  P. Tseng Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization , 2001 .

[6]  Marc Moonen,et al.  Robustness analysis of multichannel Wiener filtering and generalized sidelobe cancellation for multimicrophone noise reduction in hearing aid applications , 2005, IEEE Transactions on Speech and Audio Processing.

[7]  Sharon Gannot,et al.  Optimal binaural LCMV beamformers for combined noise reduction and binaural cue preservation , 2014, 2014 14th International Workshop on Acoustic Signal Enhancement (IWAENC).

[8]  O. L. Frost,et al.  An algorithm for linearly constrained adaptive array processing , 1972 .

[9]  Daniel Pérez Palomar,et al.  A tutorial on decomposition methods for network utility maximization , 2006, IEEE Journal on Selected Areas in Communications.

[10]  Jont B. Allen,et al.  Image method for efficiently simulating small‐room acoustics , 1976 .

[11]  Jacob Benesty,et al.  New insights into the noise reduction Wiener filter , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[12]  Jonathan G. Fiscus,et al.  DARPA TIMIT:: acoustic-phonetic continuous speech corpus CD-ROM, NIST speech disc 1-1.1 , 1993 .

[13]  L. J. Griffiths,et al.  An alternative approach to linearly constrained adaptive beamforming , 1982 .

[14]  Marc Moonen,et al.  Acoustic Beamforming for Hearing Aid Applications , 2010 .