Objectives: To compare the Naida CI UltraZoom adaptive beamformer and T-Mic settings in a real life environment.
Methods: Speech reception thresholds (SRTs) were measured in a moderately reverberant room, using the German Oldenburger sentence test. The speech signal was always presented from the front loudspeaker at 0° azimuth and fixed masking noise was presented either simultaneously from all eight loudspeakers around the subject at 0°, ±45°, ±90°, ±135°, and 180° azimuth or from five loudspeakers positioned at ±70°, ±135°, and 180° azimuth. In the third test setup, an additional roving noise was added to the six loudspeaker arrangement.
Results: There was a significant difference in mean SRTs between the Naida CI T-Mic and UltraZoom in each of the three test setups. The largest improvements were seen in the six speaker roving and fixed noise conditions. Adding ClearVoice to the Naida CI T-Mic setting significantly improved the SRT in both fixed noise conditions, but not in the roving noise condition. In each setup, the lowest SRTs were obtained with the UltraZoom plus ClearVoice setting.
Discussion: The degree of improvement was consistent with previous beamforming studies. In the most challenging listening situation, with noise from eight speakers and speech and noise presented coincidentally from the front, UltraZoom still provided a significant benefit. When a moving noise source was added, the improvement in SRT provided by UltraZoom was maintained.
Conclusion: When tested in challenging and realistic noise environments, the Naida CI UltraZoom adaptive beamformer resulted in significantly lower mean SRTs than when the T-Mic alone was used.
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http://dx.doi.org/10.1179/1754762814Y.0000000088 | DOI Listing |
J Acoust Soc Am
January 2025
Department of Informatics, University of Oslo, 0316 Oslo, Norway.
In adaptive beamforming, the array signal processing adjusts its sensor delays and weights based on the incoming data. In conventional beamforming, these parameters are instead given from a predefined model. Adaptive beamformers can improve measurement precision by dynamically rejecting spatial interference.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Huawei Technologies Co., Ltd., Chengdu 610000, China.
Metasurface-based imaging is attractive due to its low hardware costs and system complexity. However, most of the current metasurface-based imaging systems require stochastic wavefront modulation, complex computational post-processing, and are restricted to 2D imaging. To overcome these limitations, we propose a scanning virtual aperture imaging system.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Department of Digital Industry Technologies, National and Kapodistrian University of Athens, Dirfies Messapies, 34400 Athens, Greece.
The goal of the study presented in this work is to evaluate the performance of a proposed adaptive beamforming approach when combined with non-orthogonal multiple access (NOMA) in cell-free massive multiple input multiple output (CF m-MIMO) orientations. In this context, cooperative beamforming is employed taking into consideration the geographically adjacent access points (APs) of a virtual cell, aiming to minimize co-channel interference (CCI) among mobile stations (MSs) participating in NOMA transmission. Performance is evaluated statistically via extensive Monte Carlo (MC) simulations in a two-tier wireless orientation.
View Article and Find Full Text PDFBiomed Opt Express
December 2024
School of Optoelectronic, Chongqing University of Posts and Telecommunications, Chongqing, China.
In photoacoustic imaging (PAI), a delay-and-sum (DAS) beamforming reconstruction algorithm is widely used due to its ease of implementation and fast execution. However, it is plagued by issues such as high sidelobe artifacts and low contrast, that significantly hinder the ability to differentiate various structures in the reconstructed images. In this study, we propose an adaptive weighting factor called spatial coherence mean-to-standard deviation factor (scMSF) in DAS, which is extended into the spatial frequency domain.
View Article and Find Full Text PDFPeerJ Comput Sci
October 2024
Center of Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
The emergence of 6G networks promises ultra-high data rates and unprecedented connectivity. However, the effective utilization of the millimeter-wave (mmWave) as a critical enabler of foreseen potential in 6G, poses significant challenges due to its unique propagation characteristics and security concerns. Deep learning (DL)/machine learning (ML) based approaches emerged as potential solutions; however, DL/ML contains centralization and data privacy issues.
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