Performance evaluation of adaptive dual microphone systems

Chen, Jianfeng; Phua, Koksoon; Li, Shue; Sun, Hanwu

doi:10.1016/j.specom.2009.06.002

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…The microphones can be placed linearly in broadside array type or end-fire array pattern, in a line, or an arc or 3-D manner. This influences the performance of the multi-microphone signal enhancement algorithms [10][11][12]. In order to avoid spatial aliasing the microphone spacing d min which in turn depends on the maximum frequency f max and the speed of sound c. Now delay-and-sum beamformer and MVDR beamformer has been covered in context of noise reduction and signal enhancement.…”

Section: Beamforming-mentioning

confidence: 99%

Dual Microphone Beamforming Algorithm for Acoustic Signals

Agrawal¹,

Sudan²

2015

IJCA

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This paper covers delay-and-sum beamformer and Filter and sum beamformer -Minimum Variance Distortion-less Response (MVDR) beamformer. Both the beamformers were simulated and tested in terms of noise source separation at various frequencies and computational complexity using MATLAB. Even though Generalized Side-Lobe Cancellers (GSC), Superdirectivity and Post-Filtering are also available. It actually covers two-sensor array beamforming which can be extended to multisensory array. MVDR beamformer gave better results as compared to delay-and-sum beamformer, as it adopts to noise condition and also improves the beamformer output, but has a higher computational complexity. Seeing the simulation results, MVDR proves to be a better option for implementing on smart phone applications.

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Section: Beamforming-mentioning

confidence: 99%

Dual Microphone Beamforming Algorithm for Acoustic Signals

Agrawal¹,

Sudan²

2015

IJCA

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“…Among them, two-microphone noise reduction has attracted more attention recently for mobile headsets, hearing aids, or other compact distant-talking speech recognition applications because of their low-cost implementation and acceptable performance. Numerous dual-channel algorithms have already been proposed, such as the adaptive-based method [5], the subtractive-beamformer-based method [6], [7], the coherence-based method [8]- [14], the power level difference-based method [15]- [18], and the phase-based method [19]- [21].…”

Section: Introductionmentioning

confidence: 99%

Two-Microphone Noise Reduction Using Spatial Information-Based Spectral Amplitude Estimation

Guo

et al. 2012

IEICE Trans. Inf. & Syst.

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SUMMARYTraditional two-microphone noise reduction algorithms to deal with highly nonstationary directional noises generally use the direction of arrival or phase difference information. The performance of these algorithms deteriorate when diffuse noises coexist with nonstationary directional noises in realistic adverse environments. In this paper, we present a two-channel noise reduction algorithm using a spatial information-based speech estimator and a spatial-information-controlled soft-decision noise estimator to improve the noise reduction performance in realistic nonstationary noisy environments. A target presence probability estimator based on Bayes rules using both phase difference and magnitude squared coherence is proposed for soft-decision of noise estimation, so that they can share complementary advantages when both directional noises and diffuse noises are present. Performances of the proposed two-microphone noise reduction algorithm are evaluated by noise reduction, log-spectral distance (LSD) and word recognition rate (WRR) of a distant-talking ASR system in a real room's noisy environment. Experimental results show that the proposed algorithm achieves better noises suppression without further distorting the desired signal components over the comparative dual-channel noise reduction algorithms. key words: microphone array, phase difference, magnitude squared coherence, spectral amplitude estimator

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“…Generally, three types of noise fields are investigated in multi-microphones speech enhancement studies: (1) incoherent noise caused by the microphone circuitry, (2) coherent noise generated by a single well-defined directional noise source and characterized by high correlation between noise signals (3) diffuse noise, which is characterized by uncorrelated noise signals of equal power propagating in all directions simultaneously [3]. Performance of speech enhancement methods is strongly dependent on the characteristics of the environmental noise they are tested in.…”

Section: Introductionmentioning

confidence: 99%

A Dual-Microphone Speech Enhancement Algorithm Based on the Coherence Function

Yousefian

Loizou

2011

IEEE Trans. Audio Speech Lang. Process.

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A novel dual-microphone speech enhancement technique is proposed in the present paper. The technique utilizes the coherence between the target and noise signals as a criterion for noise reduction and can be generally applied to arrays with closely-spaced microphones, where noise captured by the sensors is highly correlated. The proposed algorithm is simple to implement and requires no estimation of noise statistics. In addition, it offers the capability of coping with multiple interfering sources that might be located at different azimuths. The proposed algorithm was evaluated with normal hearing listeners using intelligibility listening tests and compared against a well-established beamforming algorithm. Results indicated large gains in speech intelligibility relative to the baseline (front microphone) algorithm in both single and multiple-noise source scenarios. The proposed algorithm was found to yield substantially higher intelligibility than that obtained by the beamforming algorithm, particularly when multiple noise sources or competing talker(s) were present. Objective quality evaluation of the proposed algorithm also indicated significant quality improvement over that obtained by the beamforming algorithm. The intelligibility and quality benefits observed with the proposed coherence-based algorithm make it a viable candidate for hearing aid and cochlear implant devices.

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Performance evaluation of adaptive dual microphone systems

Cited by 7 publications

References 22 publications

Dual Microphone Beamforming Algorithm for Acoustic Signals

Dual Microphone Beamforming Algorithm for Acoustic Signals

Two-Microphone Noise Reduction Using Spatial Information-Based Spectral Amplitude Estimation

A Dual-Microphone Speech Enhancement Algorithm Based on the Coherence Function

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