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"Co-Prime Microphone Arrays: Geometry, Beamforming and Speech Direction" by Jiahong Zhao

A clear recording of speech is increasingly crucial for human-machine interaction with the swift development of modern electronic and smart devices employing artificial intelligence (AI) techniques, such as robots, autonomous vehicles and smart home assistants. Moreover, the outbreak of the unprecedented coronavirus disease (COVID) leads to a growing demand of remote applications in nearly all industries, including teleconferencing, remote teaching, telemedicine, hands-free telephony, mobile apps, etc. All these machines and applications cannot be successful without clear speech recordings. However, there is always noise, echoes and other interfering sounds in the real world, and the recording and processing of the target speech is highly challenging. Therefore, a high-quality, high-efficiency and low-cost speech recording and processing method with robustness to adverse environments is urgently needed.
Existing recording approaches use an array of microphones that are uniformly spa ....

Co Prime Microphone Array , Patial Aliasing , Rray Gain , Irection Of Arrival Estimation , Deep Learning ,

"Sparsity and nonnegativity constrained krylov approach for direction o" by Hamza Baali, Abdesselam Bouzerdoum et al.

The conventional delay-and-sum beamforming technique results in blurred source maps due to its poor spatial resolution and high side-lobe levels. To overcome these limitations, the deconvolution approach for the mapping of acoustic sources (DAMAS) has been proposed as a postprocessing stage for image enhancement. DAMAS solves an inverse problem in the form of a system of linear equations. However, this is computationally intensive. This paper presents an approach that imposes two additional constraints to the inverse problem, namely sparsity and nonnegativity of the solution. The resulting constrained problem is solved within the Krylov projection framework. Moreover, the mapping of the sparsity penalty into the Krylov subspace is approximated by a sequence of l2-norm problems via the iteratively reweighted norm (IRN) approach. Experimental results are presented which demonstrate the merits of the proposed method compared to several state-of-the-art approaches in terms of reconstructio ....

Arnoldi Algorithm , Parsity Reconstruction ,