In recent years, signal processing with ad hoc microphone arrays has attracted a lot of attention. Speech enhancement in noisy, interfered, and reverberant environments is one of the problems targeted by ad hoc microphone arrays. Most of the proposed solutions require knowledge of fingerprints, such as acoustic transfer functions, which may not be known as accurately as required in practical situations. In this paper, a distributed signal subspace filtering method is proposed which is not restricted to a special graph topology. Here, the maximum signal to interference-plus-noise ratio (max-SINR) criterion is used with the primal-dual method of multipliers for distributed filtering. The paper investigates the convergence of the algorithm in both synchronous and asynchronous schemes, and also discusses some practical pros and cons. The applicability of the proposed method is demonstrated by means of simulation results.@en

In the recent years, there have been an increasing amount of researches aiming at optimal beamforming with ad hoc microphone arrays, mostly fusion-center-based schemes. However, huge computational complexities and communication overheads impede many of these algorithms from being useful in practice. In this paper, we propose a low-footprint optimization approach to reduce the convergence time and overheads for the distributed beamforming problem. We transcribe the pseudo-coherence-based beamforming which is insightful for taking into account the nature of speech. We formulate the distributed minimum variance distortionless response beamformer using the primal-dual method of multipliers. Our experiments confirm the fast convergence using the proposed distributed algorithm. It is also shown how a hard limit on the number of iterations affects the performance of the array in noise and interference suppression.@en