Rate-constrained collaborative noise reduction for wireless hearing aids
IEEE Transactions on Signal Processing
Robust distributed noise reduction in hearing aids with external acoustic sensor nodes
EURASIP Journal on Advances in Signal Processing - Special issue on digital signal processing for hearing instruments
Theoretical analysis of binaural multimicrophone noise reduction techniques
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Generalized Spherical Array Beamforming for Binaural Speech Reproduction
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Distributed Delay and Sum Beamformer for Speech Enhancement via Randomized Gossip
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Location Feature Integration for Clustering-Based Speech Separation in Distributed Microphone Arrays
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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In a binaural hearing aid system, output signals need to be generated for the left and the right ear. Using the binaural multichannel Wiener filter (MWF), which exploits all microphone signals from both hearing aids, a significant reduction of background noise can be achieved. However, due to power and bandwidth limitations of the binaural link, it is typically not possible to transmit all microphone signals between the hearing aids. To limit the amount of transmitted information, this paper presents reduced-bandwidth MWF-based noise reduction algorithms, where a filtered combination of the contralateral microphone signals is transmitted. A first scheme uses a signal-independent beamformer, whereas a second scheme uses the output of a monaural MWF on the contralateral microphone signals and a third scheme involves an iterative distributed MWF (DB-MWF) procedure. It is shown that in the case of a rank-1 speech correlation matrix, corresponding to a single speech source, the DB-MWF procedure converges to the binaural MWF solution. Experimental results compare the noise reduction performance of the reduced-bandwidth algorithms with respect to the benchmark binaural MWF. It is shown that the best performance of the reduced-bandwidth algorithms is obtained by the DB-MWF procedure and that the performance of the DB-MWF procedure approaches quite well the optimal performance of the binaural MWF.