Continuously steerable second-order differential microphone arrays

Byun, Joon; Park, Young-Cheol; Park, Sung Wook

doi:10.1121/1.5027500

Cited by 21 publications

(4 citation statements)

References 7 publications

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“…where 0 ≤ α ≤ 1 is a real coefficient that determines the level of compromise. For the beamformer h (P ) applied to the difference pressure observations y (P ) , we attempt to maximize the corresponding WNG in (18), which is obtained from the following optimization:…”

Section: Separate Optimizationmentioning

confidence: 99%

“…Microphone array beamforming has been extensively studied and many beamforming methods have been proposed in the literature [1][2][3][4][5][6], such as superdirective beamforming [7][8][9], adaptive beamforming [10][11][12], and differential beamforming [13][14][15][16][17][18][19]. Among those, differential beamforming has attracted dramatic interest [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Combined Differential Beamforming With Uniform Linear Microphone Arrays

Huang

Wang

Benesty

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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While differential beamformers have been widely used in voice communication and human-machine speech interface systems to enhance speech signals of interest, how to design such beamformers that on the one hand can achieve the highest possible directivity factor (DF) and on the other hand are able to obtain a certain level of white noise gain (WNG), so that they are robust enough to sensors' self noise and array imperfections is still a challenging issue. This paper studies the problem of robust differential beamforming with small-size arrays to achieve a high DF. It presents a method for the design of differential beamformers with uniform linear arrays. We first generate differential pressure signals by applying the recently developed forward spatial difference operator to the outputs of the array with pressure sensors. The pressure microphone observation signals and the differential pressure signals are then put together, and a combined beamformer is subsequently designed, which consists of two sub-beamformers, one operates on the pressure microphone observations and the other on the differential pressure signals. A new class of combined differential beamformers are introduced, which can achieve different levels of compromises between DF and WNG using an adjustable parameter.

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Section: Separate Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combined Differential Beamforming With Uniform Linear Microphone Arrays

Huang

Wang

Benesty

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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show abstract

“…Further specifications include the microphone arrays' configuration variations in space, denoted as first, second [37], third degrees, or higher. The structural design also varies from planar, circular [4,38,39], spherical [40], to hybrid structures [41,42].…”

Section: Differential Microphone Arrays (Dmas)mentioning

confidence: 99%

Facial Muscle Activity Recognition with Reconfigurable Differential Stethoscope-Microphones

Bello

Zhou

Lukowicz

2020

Sensors

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Many human activities and states are related to the facial muscles’ actions: from the expression of emotions, stress, and non-verbal communication through health-related actions, such as coughing and sneezing to nutrition and drinking. In this work, we describe, in detail, the design and evaluation of a wearable system for facial muscle activity monitoring based on a re-configurable differential array of stethoscope-microphones. In our system, six stethoscopes are placed at locations that could easily be integrated into the frame of smart glasses. The paper describes the detailed hardware design and selection and adaptation of appropriate signal processing and machine learning methods. For the evaluation, we asked eight participants to imitate a set of facial actions, such as expressions of happiness, anger, surprise, sadness, upset, and disgust, and gestures, like kissing, winkling, sticking the tongue out, and taking a pill. An evaluation of a complete data set of 2640 events with 66% training and a 33% testing rate has been performed. Although we encountered high variability of the volunteers’ expressions, our approach shows a recall = 55%, precision = 56%, and f1-score of 54% for the user-independent scenario(9% chance-level). On a user-dependent basis, our worst result has an f1-score = 60% and best result with f1-score = 89%. Having a recall ≥60% for expressions like happiness, anger, kissing, sticking the tongue out, and neutral(Null-class).

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“…Among the numerous beamforming methods developed in the literature, one of the most widely used ones is the differential beamforming [8][9][10][11][12][13], which can achieve high spatial gain and frequency-invariant spatial response [14][15][16]. An important issue with the design of differential beamformers is the beampattern steering flexibility, which motivates the use of circular microphone arrays (CMAs) [17][18][19][20][21]. However, CMAsbased differential beamformers often suffer from the problem of irregularity, which leads to irregular beampatterns and deep nulls in array gains [3,22].…”

Section: Introductionmentioning

confidence: 99%

Robust Steerable Differential Beamformers with Null Constraints for Concentric Circular Microphone Arrays

Wang

Huang

Cohen

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Differential beamformers with concentric circular microphone arrays (CCMAs) are desirable for use in various applications since they can form frequency-invariant spatial responses, have better beam steering flexibility than linear arrays, and suffer less with beampattern irregularity and white noise amplification than circular microphone arrays (CMAs). The methods developed previously for differential beamforming with CCMAs are based on the series expansion. Such methods need to know the analytic form of the target beampattern, which may not be accessible in practice. Furthermore, expansion error may lead to erroneous solution, which can cause noise amplification instead of reduction. In this paper, we extend our recently developed beamforming method for CMAs to the design of differential beamformers with CC-MAs, which takes advantage of the symmetric null constraints from the beampattern. Simulations are performed to justify the properties of the proposed approach.

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Continuously steerable second-order differential microphone arrays

Cited by 21 publications

References 7 publications

Combined Differential Beamforming With Uniform Linear Microphone Arrays

Combined Differential Beamforming With Uniform Linear Microphone Arrays

Facial Muscle Activity Recognition with Reconfigurable Differential Stethoscope-Microphones

Robust Steerable Differential Beamformers with Null Constraints for Concentric Circular Microphone Arrays

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