Matrix computations (3rd ed.)
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and 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
IEEE Transactions on Signal Processing
Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis
IEEE Transactions on Signal Processing - Part II
Optimal dimensionality reduction of sensor data in multisensor estimation fusion
IEEE Transactions on Signal Processing
Binaural Noise Reduction Algorithms for Hearing Aids That Preserve Interaural Time Delay Cues
IEEE Transactions on Signal Processing
Incremental Adaptive Strategies Over Distributed Networks
IEEE Transactions on Signal Processing
Distributed Estimation Using Reduced-Dimensionality Sensor Observations
IEEE Transactions on Signal Processing
Diffusion Recursive Least-Squares for Distributed Estimation Over Adaptive Networks
IEEE Transactions on Signal Processing
Reduced-Bandwidth and Distributed MWF-Based Noise Reduction Algorithms for Binaural Hearing Aids
IEEE Transactions on Audio, Speech, and Language 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)
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We introduce a distributed adaptive algorithm for linear minimum mean squared error (MMSE) estimation of node-specific signals in a fully connected broadcasting sensor network where the nodes collect multichannel sensor signal observations. We assume that the node-specific signals to be estimated share a common latent signal subspace with a dimension that is small compared to the number of available sensor channels at each node. In this case, the algorithm can significantly reduce the required communication bandwidth and still provide the same optimal linear MMSE estimators as the centralized case. Furthermore, the computational load at each node is smaller than in a centralized architecture in which all computations are performed in a single fusion center. We consider the case where nodes update their parameters in a sequential round robin fashion. Numerical simulations support the theoretical results. Because of its adaptive nature, the algorithm is suited for real-time signal estimation in dynamic environments, such as speech enhancement with acoustic sensor networks.