Optimal Modal Beamforming for Spherical Microphone Arrays

Yan, Shefeng; Sun, Haohai; Svensson, U. Peter; Ma, Xiaochuan; Hovem, Jens M.

doi:10.1109/tasl.2010.2047815

Cited by 124 publications

(48 citation statements)

References 21 publications

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“…For PIV, we have 48 (3 × 3 × 4 + 3 × 4) operations where the numbers in parentheses respectively represent the number of axes, harmonic modes and the real multiplications in (14) and the number of axes and the real multiplications in (13).…”

Section: Computational Complexitymentioning

confidence: 99%

“…(1) Steered Response Power (SRP) methods [21] in which a beamformer such as Plane-Wave Decomposition (PWD) [22], [14], [11] is used to steer a beam into multiple directions and the source direction is found as the direction with the highest power. Due to limitations on spatial resolution, SRP methods can fail to localize closely spaced sources [23]; (2) Sub-space methods [24], [25] such as MUSIC (MUltiple SIgnal Classification) [6], [26] employ the eigenvalue decomposition to decompose the noisy signals spatial covariance matrix into the signal and noise subspaces, which then are used to estimate the DOAs.…”

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confidence: 99%

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Augmented Intensity Vectors for Direction of Arrival Estimation in the Spherical Harmonic Domain

Hafezi

Moore

Naylor

2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-Pseudointensity vectors (PIVs) provide a means of Direction of Arrival (DOA) estimation for Spherical Microphone Arrays (SMAs) using only the zeroth and the first-order spherical harmonics. An Augmented Intensity Vector (AIV) is proposed which improves the accuracy of PIVs by exploiting higher order spherical harmonics. We compared DOA estimation using our proposed AIVs against PIVs, Steered Response Power (SRP) and subspace methods where the number of sources, their angular separation, the reverberation time of the room and the sensor noise level are varied. The results show that the proposed approach outperforms the baseline methods and performs at least as accurately as the state-of-the-art method with strong robustness to reverberation, sensor noise and number of sources. In the single and multiple source scenarios tested, which include realistic levels of reverberation and noise, the proposed method had average error of 1.5°and 2°, respectively.

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Section: Computational Complexitymentioning

confidence: 99%

mentioning

confidence: 99%

Augmented Intensity Vectors for Direction of Arrival Estimation in the Spherical Harmonic Domain

Hafezi

Moore

Naylor

2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…With the aim of walking in a remote space while keeping this perceived information, we can also reproduce a sound field simulated with source signals and sound source positions that are also obtained using various types of microphones [37]. Even enhanced monaural speech signals generated by a beamforming technique can be used as source signals [38], [39].…”

Section: Immersionmentioning

confidence: 99%

Virtual Reality Technologies in Telecommunication Services

Nagata

Mikami

Miyashita

et al. 2017

Journal of Information Processing

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Telecommunication service has been growing and progressing from telephone to high reality communication systems that are based on evolution of network and media technologies. Recognizing virtual reality (VR) as a communication tool, we provide a review of communication services and the directions they are moving in, as well as related VR technologies. The Immersive Telepresence System "Kirari!" is also introduced as the latest development example for a new telecommunication service.

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“…For PIV, we have 48 (3 × 3 × 4 + 3 × 4) operations where the numbers in parentheses respectively represent the number of axes, harmonic modes and the real multiplications in (6) and the number of axes and the real multiplications in (5).…”

Section: Computation Complexitymentioning

confidence: 99%

“…A range of methods for source localization using SMAs has been studied over the past decades and can be categorised into four groups of steered response power methods [2,[5][6][7],…”

Section: Introductionmentioning

confidence: 99%

Multiple source localization in the spherical harmonic domain using augmented intensity vectors based on grid search

Hafezi

Moore

Naylor

2016

2016 24th European Signal Processing Conference (EUSIPCO)

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Multiple source localization is an important task in acoustic signal processing with applications including dereverberation, source separation, source tracking and environment mapping. When using spherical microphone arrays, it has been previously shown that Pseudo-intensity Vectors (PIV), and Augmented Intensity Vectors (AIV), are an effective approach for direction of arrival estimation of a sound source. In this paper, we evaluate AIV-based localization in acoustic scenarios involving multiple sound sources. Simulations are conducted where the number of sources, their angular separation and the reverberation time of the room are varied. The results indicate that AIV outperforms PIV and Steered Response Power (SRP) with an average accuracy between 5 and 10 degrees for sources with angular separation of 30 degrees or more. AIV also shows better robustness to reverberation time than PIV and SRP.

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Optimal Modal Beamforming for Spherical Microphone Arrays

Cited by 124 publications

References 21 publications

Augmented Intensity Vectors for Direction of Arrival Estimation in the Spherical Harmonic Domain

Augmented Intensity Vectors for Direction of Arrival Estimation in the Spherical Harmonic Domain

Virtual Reality Technologies in Telecommunication Services

Multiple source localization in the spherical harmonic domain using augmented intensity vectors based on grid search

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