Near-field beamforming for microphone arrays

Ryan, James G.; Goubran, Rafik

doi:10.1109/icassp.1997.599648

Cited by 34 publications

(21 citation statements)

References 4 publications

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“…By assuming the nearfield spherically isotropic noise field, i.e., having uniformly distributed noise sources on a sphere of radius , we can write the total noise power received as 1070-9908/99$10.00 © 1999 IEEE , where is a unit vector in the direction of and the integration is over all directions. We define the matrix with elements (4) (Note that is Hermitian and positive definite). Then, , and (1) becomes a ratio of quadratic forms (5) The usual goal is to find which maximizes .…”

Section: Gain Optimization For An Arbitrary Arraymentioning

confidence: 99%

“…Thus, the overall noise field is mainly due to nearfield noise sources, and an assumption of farfield spherically isotropic noise or white Gaussian noise is a very crude approximation. In [4], the farfield spherically isotropic noise was used to model the effect of reverberation without considering the effect of nearfield noise sources. Publisher Item Identifier S 1070-9908(99)05800-9.…”

Section: Introductionmentioning

confidence: 99%

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Noise modeling for nearfield array optimization

Abhayapala

Kennedy

Williamson

1999

IEEE Signal Process. Lett.

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Section: Gain Optimization For An Arbitrary Arraymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Noise modeling for nearfield array optimization

Abhayapala

Kennedy

Williamson

1999

IEEE Signal Process. Lett.

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“…As a consequence, one needs to account for the near-field effects. Examples where this happens include near-field higher-order Ambisonics [5] and near-field beamforming [14].…”

Section: A) Spherical Radiationmentioning

confidence: 99%

Digital acoustics: processing wave fields in space and time using DSP tools

Pinto

Kolundžija

Vetterli

2014

SIP

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I . I N T R O D U C T I O NIn the world of digital signal processing (DSP), there are three fundamental tools that have become the basis of every algorithm, system, and theory dealing with the processing of digital audio. Those are the Nyquist sampling theory, the Fourier transform, and digital filtering. We could add a fourth one -the short time Fourier transform -which generalizes the Fourier transform to account for non-stationary signals such as music and speech. These concepts are so embedded into the creative thinking of audio engineers and scientists that new ideas are often intuitively based on one (or more) of these fundamental tools. Digital audio coding, speech synthesis, and adaptive echo cancellation are great examples of complex systems built on the theories of sampling, Fourier analysis, and digital filtering.Fourier theory itself is built on some of the most basic tools of mathematics, such as vector spaces and integration theory (although harmonic analysis was not originally conceived this way by Joseph Fourier [1]). From an intuitive perspective, the Fourier transform can be seen as a change of representation obtained by projecting the input signal s (t) onto an orthogonal set of complex exponential functions ϕ(ω, t) = e − j ωt , given by S(ω) = R s (t)ϕ(ω, t)dt. The Fourier representation is useful for many types of signals, and is oftentimes the logical choice. As a consequence, many of the mathematical and computational tools available today for the purpose of DSP have been developed

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“…The coupling function can be calculated using the beamformer response function. It is this function that determines the spectral information of the interfering source signals that are filtered through by the beamformer and is given by: (5) This is derived from the DS beamformer and is a function of frequency and source direction. Given the beamformer function, it is possible to evaluate the coupling function across frequencies that can directly determine the spectral characteristics of the interfering sources passed through the beamformer.…”

Section: Coupling Function Kmentioning

confidence: 99%

“…This paper develops a new method of utilizing three beamformers, with a coupled post-processing enhancement algorithm, to extract each speech source signal. The fixed beamformers used have far-field assumptions as given in [5] and narrowband approximations through the use of frequency bands as given in [6].…”

Section: Introductionmentioning

confidence: 99%

Combining multisource wiener filtering with parallel beamformers to reduce noise from interfering talkers

Ewalt

Johnson

Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004.

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Abstract:The ability to extract a primary speech signal from an environment with multiple speakers is an important issue in speech enhancement [1,2]. This paper presents a method for incorporating multiple parallel beamformers with a Wiener filter.By iteratively improving the spectral magnitude estimates of each speech source, substantial improvement in overall signal separation can be obtained. The performance of the algorithm is illustrated using a simulated multiple speaker environment with resulting SNR and sSNR plots.

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Near-field beamforming for microphone arrays

Cited by 34 publications

References 4 publications

Noise modeling for nearfield array optimization

Noise modeling for nearfield array optimization

Digital acoustics: processing wave fields in space and time using DSP tools

Combining multisource wiener filtering with parallel beamformers to reduce noise from interfering talkers

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