Experimental Evaluation of a Nested Microphone Array With Adaptive Noise Cancellers

Zheng, Yu; Goubran, Rafik; El-Tanany, M.

doi:10.1109/tim.2004.827304

Cited by 39 publications

(28 citation statements)

References 10 publications

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“…Then the covariance matrix defined in (8) can be computed numerically (12) where , and is a real matrix formed by selecting points in the spatial region and points in the frequency band (13) here and are, respectively, the real and imaginary part of the steering vector at the point defined in (1). Meanwhile, the beamformer responses at these points can be controlled by defining the constraint equation as (14) where is the desired response vector defined by (15) and is the group delay of the signal location relative to the array coordinate origin and are the desired amplitude responses. Unit gain response is enforced by setting , and the group delays to the temporal center of the beamformer.…”

Section: B Near-field Regionally Constrained Adaptive Beamformingmentioning

confidence: 99%

“…The subarray elements were super-imposed and harmonically nested resulting in a total of 17 elements. The multirate subband beamforming technique [14] was then employed with three subband robust adaptive beamformers designed using the proposed regionally constrained near-field beamforming method. The presumed focal point was .…”

Section: Performance In Reverberant Environmentmentioning

confidence: 99%

“…However, frequent offline calibration is required to account for the changing environment. An alternative approach is the two-stage method [13], [14] which uses several fixed-weight near-field beamformers in the first stage to focus on the desired and interfering signals. It then employs a standard adaptive noise canceller in the second stage to adaptively suppress the noise and interference.…”

mentioning

confidence: 99%

“…The proposed beamformer can suppressed the near or far field interfering signals by more than 20 dB. For applications in reverberant environments, the proposed near-field adaptive beamforming method is applied to a nested linear array [14] consisting of seventeen elements which is subbanded into three subarrays for the speech frequency band . The compound array achieves good de-reverberation gain of 5 to 7 dB in the two lower subbands.…”

mentioning

confidence: 99%

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Robust Near-Field Adaptive Beamforming With Distance Discrimination

Zheng

Goubran

El-Tanany

2004

IEEE Trans. Speech Audio Process.

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Abstract-This paper proposes a robust near-field adaptive beamformer for microphone array applications in small rooms. Robustness against location errors is crucial for near-field adaptive beamforming due to the difficulty in estimating near-field signal locations especially the radial distances. A near-field regionally constrained adaptive beamformer is proposed to design a set of linear constraints by filtering on a low rank subspace of the near-field signal over a spatial region and frequency band such that the beamformer response over the designed spatial-temporal region can be accurately controlled by a small number of linear constraint vectors. The proposed constraint design method is a systematic approach which guarantees real arithmetic implementation and direct time domain algorithms for broadband beamforming. It improves the robustness against large errors in distance and directions of arrival, and achieves good distance discrimination simultaneously. We show with a nine-element uniform linear array that the proposed near-field adaptive beamformer is robust against distance errors as large as 32% of the presumed radial distance and angle errors up to 20 . It can suppress a far field interfering signal with the same angle of incidence as a near-field target by more than 20 dB with no loss of the array gain at the near-field target. The significant distance discrimination of the proposed near-field beamformer also helps to improve the dereverberation gain and reduce the desired signal cancellation in reverberant environments.Index Terms-Dereverberation, distance discrimination, interference suppression, microphone array, near-field beamforming, regionally constrained beamforming, robust adaptive beamforming, robustness against location errors.

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Section: B Near-field Regionally Constrained Adaptive Beamformingmentioning

confidence: 99%

Section: Performance In Reverberant Environmentmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Robust Near-Field Adaptive Beamforming With Distance Discrimination

Zheng

Goubran

El-Tanany

2004

IEEE Trans. Speech Audio Process.

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“…We also propose a new nested circular microphone array to eliminate spatial aliasing more effectively. To our knowledge, the nested arrays have already been designed and examined only in linear structure and for speech enhancement [38]. In current research, we present a new structure of nested microphone array which has a circular arrangement and can be used in speaker localization.…”

Section: Introductionmentioning

confidence: 99%

A Novel Nested Circular Microphone Array and Subband Processing-Based System for Counting and DOA Estimation of Multiple Simultaneous Speakers

Firoozabadi

Abutalebi

2015

Circuits Syst Signal Process

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This paper addresses the topic of simultaneous speaker localization. The work is related to the generalized cross-correlation (GCC)-based methods for estimating the direction of multiple speakers. Considering the defects of GCC-based direction of arrival (DOA) estimation methods, we have applied several modifications to improve our previous subband processing-based system for the localization of simultaneous speakers. Three modifications have been presented in this paper. In the first step, the DOA estimation method is equipped with a front-end block that determines the number of speakers based on K-means clustering and silhouette criterion. This block provides the true number of speakers for the DOA estimator. Secondly, in order to eliminate the spatial aliasing, we propose a novel nested circular microphone array. In the proposed array design, each microphone pair is only used in appropriate subband according to its inter-microphone distance. In the third step, to overcome the weakness of GCC-phase transform (GCC-PHAT) in noisy and noisy-reverberant conditions, we propose a SNR estimation block. So, we can separate noisy and reverberant conditions and use PHAT filter for reverberant conditions and maximum likelihood filter for noisy situations. The proposed method has been evaluated on both simulated and real multi-speaker speech data in various environmental conditions and different number of speakers. Our evaluations in terms of DOA accuracy demonstrate the superiority of the proposed method compared to the fullband and baseline subband methods.

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