Differential and Constant-Beamwidth Beamforming with Uniform Rectangular Arrays

Itzhak, Gal; Cohen, Israel

doi:10.1109/iwaenc53105.2022.9914769

Cited by 4 publications

(6 citation statements)

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“…For example, focusing on the endfire direction (ϕ 0 = 0 o with respect to the positive x-axis direction), we assume a wide potential range of DOA deviations on the x-y plane of all angles satisfying |ϕ| ≤ ϕ H . We note that unlike previous studies, which considered a relatively narrow deviation range (ϕ H ≈ 20 o − 30 o ) [33], [35], [36], in this study, we target more severe cases which require a broader range. To achieve this, we first take advantage of the microphone array topology optimization approach suggested in [35] with ULAs and in [36] with nonuniform rectangular arrays and optimize a K-sparse CCA structure out of a UCCA of M possible microphone locations (uniformly distributed and equally spaced on the N UCCA's rings, as elaborated in Section II).…”

Section: Scca-based Kp Beamformersmentioning

confidence: 99%

“…We note that unlike previous studies, which considered a relatively narrow deviation range (ϕ H ≈ 20 o − 30 o ) [33], [35], [36], in this study, we target more severe cases which require a broader range. To achieve this, we first take advantage of the microphone array topology optimization approach suggested in [35] with ULAs and in [36] with nonuniform rectangular arrays and optimize a K-sparse CCA structure out of a UCCA of M possible microphone locations (uniformly distributed and equally spaced on the N UCCA's rings, as elaborated in Section II). The topology optimization is performed concerning the broadband directivity index:…”

Section: Scca-based Kp Beamformersmentioning

confidence: 99%

“…For example, rectangular arrays (RAs) exhibit reduced susceptibility to the desired signal's DOA. Furthermore, it may enable optimization concerning several criteria at once by taking advantage of the Kronecker-product (KP) beamforming framework [12]- [16]. RA structures are also known to be valuable in differential (closely-spaced microphones) settings [17], [18] and for DOA estimation methods [19], [20].…”

Section: Introductionmentioning

confidence: 99%

“…To overcome some of the drawbacks in [35], an optimized RA topology was introduced in [36], suggesting a uniform structure along one axis and a nonuniform structure along the other. This yielded high array directivity over a desirable deviation range and a constant mainlobe beamwidth simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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Kronecker-Product Beamforming with Sparse Concentric Circular Arrays

Itzhak,

Cohen

2023

IEEE Open J. Signal Process.

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This paper presents a Kronecker-product (KP) beamforming approach incorporating sparse concentric circular arrays (SCCAs). The locations of the microphones on the SCCA are optimized concerning the broadband array directivity over a wide range of direction-of-arrival (DOA) deviations of a desired signal. A maximum directivity factor (MDF) sub-beamformer is derived accordingly with the optimal locations. Then, we propose two global beamformers obtained as a Kronecker product of a uniform linear array (ULA) and the SCCA sub-beamformer. The global beamformers differ by the type of the ULA, which is designed either as an MDF sub-beamformer along the x-axis or as a maximum white noise gain sub-beamformer along the y-axis. We analyze the performance of the proposed beamformers in terms of the directivity factor, the white noise gain, and their spatial beampatterns. Compared to traditional beamformers, the proposed beamformers exhibit considerably larger tolerance to DOA deviations concerning both the azimuth and elevation angles. Experimental results with speech signals in noisy and reverberant environments demonstrate that the proposed approach outperforms traditional beamformers regarding the perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI) scores when the desired speech signals deviate from the nominal DOA.INDEX TERMS Microphone arrays, Kronecker-product beamforming, concentric circular beamformers, direction-of-arrival deviations, sparse arrays.

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Section: Scca-based Kp Beamformersmentioning

confidence: 99%

Section: Scca-based Kp Beamformersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Kronecker-Product Beamforming with Sparse Concentric Circular Arrays

Itzhak,

Cohen

2023

IEEE Open J. Signal Process.

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“…Rectangular arrays have been demonstrated to be beneficial for direction-of-arrival estimation tasks [180]. In order to evaluate the performance of the fixed beamformer generated by our model, a comparison can be made with the beamformer obtained using the Kronecker-product (KP) [76]. The KP approach enables the decomposition of a global beamformer into independent subbeamformers, which can be individually optimized.…”

Section: Fixed Beamforming With Rectangular Arraysmentioning

confidence: 99%

Machine Learning Assisted Signal Enhancement

Yan

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In an era of abundant signals, the ability to obtain desired signals while rejecting undesired ones has become increasingly crucial. Often, the desired signals are mixed with interference or contaminated by noise. Signal enhancement techniques play a vital role in performing tasks such as signal separation, extraction, and suppression. This thesis addresses critical challenges in signal enhancement tasks by harnessing the power of machine learning techniques.Firstly, we propose a novel independence criterion called the Finite Basis Independence Criterion (FBIC). This criterion estimates the Hirschfeld-Gebelein-Rényi maximum correlation coefficient between tested variables and is based on mapping functions from a subspace of finite basis. FBIC detects dependence between variables in linear time and outperforms more computationally expensive kernel-based counterparts. Extensive testing in Independent Component Analysis benchmarks demonstrates its potential for various signal separation applications.Secondly, we conduct a comprehensive robustness analysis of a popular signal enhancement approach: fixed beamforming based on first-order linear Differential Microphone Arrays (DMAs). We demonstrate that both bounded and unbounded phase errors of microphones can affect the mainlobe orientation of the beamformer. Analytically derived white noise gain thresholds indicate when mainlobe misorientation occurs. Through rigorous mathematical derivations, we prove that a higher number of microphones and increased spacing between microphones contribute to the robustness of the beamformer. This work provides practical guidelines for designing robust first-order linear DMAs.Thirdly, we propose a neural network model to optimize both the geometry and spatial filter of linear DMAs. The model consists of two feed forward neural networks and is trained end-to-end. The signals enhanced by this model exhibit superior quality compared to those obtained from conventional DMA approaches. Furthermore, the model offers flexibility in controlling the tradeoff between different performance metrics, allowing for customized optimization.Lastly, we extend the neural network model to a general framework that allows optimization of microphone arrays of any geometry, along with their spatial filters. This model employs ResNets and augmented Lagrangian techniques to achieve state-of-the-art frequency-invariant fixed beamforming performance. We showcase our performance in linear, circular, and concentric circular microphone arrays. Moreover, our findings challenge the conventional belief that concentric circular arrays require multiple rings, as we demonstrate that good performance can be achieved with only one ring.Overall, this thesis contributes novel techniques and insights to the field of signal enhancement, leveraging machine learning approaches to address key challenges. The proposed criteria, guidelines and models have the potential to advance various signal separation applications and enhance the overall quality of processed signals.

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Constant Elevation-Beamwidth Beamforming With Concentric Ring Arrays

Peretz,

Cohen

2024

IEEE/ACM Trans. Audio Speech Lang. Process.

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Differential and Constant-Beamwidth Beamforming with Uniform Rectangular Arrays

Cited by 4 publications

References 20 publications

Kronecker-Product Beamforming with Sparse Concentric Circular Arrays

Kronecker-Product Beamforming with Sparse Concentric Circular Arrays

Machine Learning Assisted Signal Enhancement

Constant Elevation-Beamwidth Beamforming With Concentric Ring Arrays

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