Delay-and-sum beamforming for direction of arrival estimation applied to gunshot acoustics

Ramos, Antônio L. L.; Holm, Sverre; Gudvangen, S.; Otterlei, Ragnvald

doi:10.1117/12.886833

Cited by 12 publications

(7 citation statements)

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“…The literature is rich in techniques for spatial analysis of sound. Seminal work in direction of arrival estimation using microphone arrays [7,47,48,50], array signal processing for beamforming [20,36,57], and subspace-based super-resolution algorithms [53,65] have significantly advanced this field of study. In the recent past, new innovations in ubiquitous spatial acoustic sensing [13, 26, 41, 42, 49, 60-62, 67, 68] have opened up new opportunities.…”

Section: Related Workmentioning

confidence: 99%

Owlet

Garg

Bai

Roy

2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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This paper presents a low-power and miniaturized design for acoustic direction-of-arrival (DoA) estimation and source localization, called Owlet. The required aperture, power consumption, and hardware complexity of the traditional array-based spatial sensing techniques make them unsuitable for small and power-constrained IoT devices. Aiming to overcome these fundamental limitations, Owlet explores acoustic microstructures for extracting spatial information. It uses a carefully designed 3D-printed metamaterial structure that covers the microphone. The structure embeds a direction-specific signature in the recorded sounds. Owlet system learns the directional signatures through a one-time in-lab calibration. The system uses an additional microphone as a reference channel and develops techniques that eliminate environmental variation, making the design robust to noises and multipaths in arbitrary locations of operations. Owlet prototype shows 3.6 • median error in DoA estimation and 10 median error in source localization while using a 1.5 × 1.3 acoustic structure for sensing. The prototype consumes less than 100 ℎ of the energy required by a traditional microphone array to achieve similar DoA estimation accuracy. Owlet opens up possibilities of low-power sensing through 3D-printed passive structures. CCS CONCEPTS• Computer systems organization → Embedded and cyberphysical systems; Sensors and actuators.

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Section: Related Workmentioning

confidence: 99%

Owlet

Garg

Bai

Roy

2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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“…These narrowband techniques maybe used for wideband signals, for example, by subband decomposition. Also, the ‘delay‐and‐sum’ beamforming [19] maybe used in conjunction with ‘steering response power’ [19, 20] for DOA estimation of wideband signals, but it has been found [15] that it performs worst than methods that employ the ‘generalised cross‐correlation’ (GCC) technique [21]. The phase transform (PHAT) [21] subclass of the GCC technique is used in this paper and the resulting DOA estimation is briefly described as follows.…”

Section: Doa Estimationmentioning

confidence: 99%

Consistent DOA estimation of heavily noisy gunshot signals using a microphone array

Borzino

Apolinario

Campos

2016

IET Radar, Sonar & Navigation

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“…Source localization methods can be categorized as direct and indirect methods [8]. Direct methods such as delay and sum (DAS) [9], minimum power distortionless response (MPDR) [10], and multiple signal classification (MUSIC) [11] can be used to estimate the DOA directly from the microphone signals, whereas indirect methods can be used to estimate direction-bearing features such as the time difference of arrival (TDOA) [12] between microphone pairs in the first stage, and estimate the DOAs of sources in the second stage [13]. Recently, orthogonal matching pursuit (OMP)-based approaches have been proposed to estimate the M DOAs with high resolution [14], [15], where M is the number of microphone sensors.…”

Section: Introductionmentioning

confidence: 99%

Tracking of Moving Sources in a Reverberant Environment Using Evolutionary Algorithms

2022

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This paper describes a source tracking technique in a reverberant environment using a new combination of an adaptive species-based particle swarm optimization (ASPSO) algorithm and a multisignal classification (MUSIC) algorithm. To mitigate the effects of reverberation, an insightful dereverberation method based on an online autoregressive (AR) array and a minimum variance distortionless response (MVDR) beamformer is developed to dereverberate the microphone signal prior to direction of arrival (DOA) estimation using MUSIC. On the basis of several evolutionary schemes, ASPSO enables rapid tracking by finding local maxima in the MUSIC pseudospectrum. In the ASPSO algorithm, particles are divided into different species, where each species is associated with a sound source. As the sound source moves, the DOA information is dynamically updated using ASPSO, in which the inertia weight decreases progressively to prevent premature convergence. Two update rules for adapting the filter coefficients are employed for drastically moving sources. Simulations and experiments are conducted using a circular microphone array to validate the proposed ASPSO with AR (ASPSO-AR) algorithm. The results demonstrate that ASPSO-AR requires one-third of the processing time of the grid search (GS) method. In addition, the root-mean-square error (RMSE) of the ASPSO-AR algorithm is 10° less than that of the GS method.

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Delay-and-sum beamforming for direction of arrival estimation applied to gunshot acoustics

Cited by 12 publications

References 0 publications

Owlet

Owlet

Consistent DOA estimation of heavily noisy gunshot signals using a microphone array

Tracking of Moving Sources in a Reverberant Environment Using Evolutionary Algorithms

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