Beamformer configuration design in reverberant environments

On sparse beamformer design with reverberation

“…From the above definition, we can see that the θ(s, µ) defined in (10) is the smoothing function of absolute value function | • |. The following lemma shows that the smoothing function θ(s, µ) also satisfies some interesting properties [29].…”

“…It is noted that optimal beamforming arrays are usually achieved at non-uniformly configurations as indicated by the placement design problems [5,6,7,8,9,10], and the increase of microphone elements is usually not as flexible as the filter length. Indeed, most studies on beamformer design have been focused on the optimization of the filter coefficients.…”

Sensor placement optimization of blind source separation in a wireless acoustic sensor network via hybrid descent methods

“…In this paper, we propose the use of genetic algorithm. The genetic algorithm was developed in [31] to solve the sensor placement problem in the beamformer configuration design. Comparing to others, the genetic algorithm has a nice parallel computation structure in which the candidate solution is generated by the random perturbation of a population rather than by moving from one point to the next.…”

Sparse broadband beamformer design via proximal optimization Techniques

“…The classic methods of beamformer design include the minimum variance distortionless response (MVDR) beamformer design, the linearly constrained minimum variance (LCMV) beamformer design [3,4], and the generalized sidelobe canceller (GSC) [5,6]. Generally, the performance of beamforming is greatly affected by both the filter coefficients and the array configuration [7,8,9,10]. Recently, the sparsity of array configuration attracts more and more attentions, and various strategies have been developed to devise sparse arrays for different tasks including direction finding, adaptive beamforming, and beampattern synthesis [11,12,13,14].…”