Distributed signal-processing algorithms in (wireless) sensor networks often aim to decentralize processing tasks to reduce communication cost and computational complexity or avoid reliance on a single device (i.e., fusion center) for processing. In this contribution, we extend a distributed adaptive algorithm for blind system identification that relies on the estimation of a stacked network-wide consensus vector at each node, the computation of which requires either broadcasting or relaying of node-specific values (i.e., local vector norms) to all other nodes. The extended algorithm employs a distributed-averaging-based scheme to estimate the network-wide consensus norm value by only using the local vector norm provided by neighboring sensor nodes. We introduce an adaptive mixing factor between instantaneous and recursive estimates of these norms for adaptivity in a time-varying system. Simulation results show that the extension provides estimation results close to the optimal fully-connected-network or broadcasting case while reducing inter-node transmission significantly.

While substantial noise reduction and speech enhancement can be achieved with multiple microphones organized in an array, in some cases, such as when the microphone spacings are quite close, it can also be quite limited. This degradation can, however, be resolved by the introduction of one or more external microphones (XMs) into the same physical space as the local microphone array (LMA). In this paper, three methods of extending an LMA-based generalized sidelobe canceller (GSC-LMA) with multiple XMs are proposed in such a manner that the relative transfer function pertaining to the LMA is treated as a priori knowledge. Two of these methods involve a procedure for completing an extended blocking matrix, whereas the third uses the speech estimate from the GSC-LMA directly with an orthogonalized version of the XM signals to obtain an improved speech estimate via a rank-1 generalized eigenvalue decomposition. All three methods were evaluated with recorded data from an office room and it was found that the third method could offer the most improvement. It was also shown that in using this method, the speech estimate from the GSC-LMA was not compromised and would be available to the listener if so desired, along with the improved speech estimate that uses both the LMA and XMs.

Conventionally, the single constraint of the minimum variance distortionless response (MVDR) beamformer for speech enhancement has been defined using one of two approaches. Either it is based on a priori assumptions such as microphone characteristics, position, speech source location, and room acoustics, or on a relative transfer function (RTF) vector estimate using a data dependent method. Each approach has its respective merits and drawbacks and a decision usually has to be made between one of the approaches. In this paper, an alternative approach of using an integrated MVDR beamformer is investigated, where both the hard constraints from the two conventional approaches are softened to yield two tuning parameters. It will be shown that this integrated MVDR beamformer can be expressed as a convex combination of the conventional MVDR beamformers, a linearly constrained minimum variance (LCMV) beamformer, and an all-zero vector, with real, positive-valued coefficients. By analysing how the tuning parameters affect these coefficients, two tuning rules for a practical implementation of the integrated MVDR are subsequently proposed. An evaluation with simulated and recorded data demonstrates that the integrated MVDR beamformer can be beneficial as opposed to relying on either of the conventional MVDR beamformers.

The use of an external microphone in conjunction with an existing local microphone array can be particularly beneficial for noise reduction tasks that are critical for hearing devices, such as cochlear implants and hearing aids. Recent work has already demonstrated how an external microphone signal can be effectively incorporated into the common noise reduction technique of using a Minimum Variance Distortionless Response (MVDR) beamformer. In this paper, we provide a further extension, whereby an external microphone signal can be incorporated into an existing framework of a Generalised Sidelobe Canceller (GSC) that has been designed for a local microphone signal array. It will be shown that the resulting GSC with an external microphone results in an easily implementable addition to the existing GSC framework for a local microphone array, and can exhibit an improved noise reduction performance.