A high performance microphone array system for hearing aid applications

Wang, A.; Yao, Kung; Hudson, Robert S.; Korompis, D.; Lorenzelli, F.; Soli, Sig; Gao, Shuyang

doi:10.1109/icassp.1996.550556

Cited by 11 publications

(6 citation statements)

References 8 publications

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“…Equalization of the mismatched sensor responses under known conditions [27] and blind conditions [28] using various blind equalization methods proposed for wireless communication systems [29] can also be used here.…”

Section: Practical Coherent Array Processing Issuesmentioning

confidence: 99%

Coherent acoustic array processing and localization on wireless sensor networks

et al. 2003

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Section: Practical Coherent Array Processing Issuesmentioning

confidence: 99%

Coherent acoustic array processing and localization on wireless sensor networks

et al. 2003

Self Cite

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“…In recent years, due to the development of microelectromechanical system (MEMS) technology, small aperture array shows its potential superiority for portable applications. Microphone arrays for hearing aid [1], videoconferencing [2], and robotic audition [3,4] are often used in small offices or desktop applications and meanwhile high mobility equipment is required in vehicle localization [5] and gunshot localization [6] in the battlefield. Therefore, small aperture arrays have received high attention.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Source Localization via Subspace Based Method Using Small Aperture MEMS Arrays

et al. 2014

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Small aperture microphone arrays provide many advantages for portable devices and hearing aid equipment. In this paper, a subspace based localization method is proposed for acoustic source using small aperture arrays. The effects of array aperture on localization are analyzed by using array response (array manifold). Besides array aperture, the frequency of acoustic source and the variance of signal power are simulated to demonstrate how to optimize localization performance, which is carried out by introducing frequency error with the proposed method. The proposed method for 5 mm array aperture is validated by simulations and experiments with MEMS microphone arrays. Different types of acoustic sources can be localized with the highest precision of 6 degrees even in the presence of wind noise and other noises. Furthermore, the proposed method reduces the computational complexity compared with other methods.

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“…The goal of speech enhancement is to remove undesirable signals such as noise and reverberation. Among research areas in the field of speech enhancement are teleconferencing [7]- [8], hands-free telephones [9]- [11], hearing aids [12]- [21], speech recognition [22]- [23], intelligibility improvement [24]- [25], and acoustic measurement [26].…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Beamforming

Hoshuyama

Sugiyama

2001

Digital Signal Processing

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Abstract. This chapter presents robust adaptive beamforming techniques designed specifically for microphone array applications. The basics of adaptive beamformers are first reviewed with the Griffiths-Jim beamformer (GJBF). Its robustness problems caused by steering vector errors are then discussed with some conventionally proposed robust beamformers. As better solutions to the conventional robust beamformers, GJBFs with an adaptive blocking matrix are presented in the form of a microphone array. Simulation results and real-time evaluation data show that a new robust adaptive microphone array achieves improved robustness against steering vector errors. Good sound quality of the output signal is also confirmed by a subjective evaluation. IntroductionBeamforming is a technique which extracts the desired signal contaminated by interference based on directivity, Le. spatial signal selectivity [1]-[5]. This extraction is performed by processing the signals obtained by multiple sensors such as microphones, antennas, and sonar transducers located at different positions in the space. The principle of beamforming has been known for a long time. Because of tlie vast amount of necessary signal processing, most research and development effort has been focused on geological investigations and sonar, which can afford a higher cost. With the advent of LSI technology, the required amount of signal processing has become relatively small. As a result, a variety of research projects where acoustic beamforming is applied to consumer-oriented applications, have been carried out [6].Applications of beamforming include microphone arrays for speech enhancement. The goal of speech enhancement is to remove undesirable signals such as noise and reverberation. Among research areas in the field of speech enhancement are teleconferencing [7] Beamforming can be considered as multidimensional signal processing in space and time. Ideal conditions assumed in most theoretical discussions are not always maintained. The target DOA (direction of arrival), which is assumed to be stable, does change with the movement of the speaker. The sensor gains, which are assumed uniform, exhibit significant distribution. As a result, the performance obtained by beamforming may not be as good as

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A high performance microphone array system for hearing aid applications

Cited by 11 publications

References 8 publications

Coherent acoustic array processing and localization on wireless sensor networks

Coherent acoustic array processing and localization on wireless sensor networks

Acoustic Source Localization via Subspace Based Method Using Small Aperture MEMS Arrays

Robust Adaptive Beamforming

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