Robust Bayesian Beamforming for Sources at Different Distances with Applications in Urban Monitoring

An array with MEMS microphones can distinguish individual noise sources in an environment through spatial filtering. Its effectiveness depends on the variations in microphone sensitivity and phase. Quantification of these variations is valuable, because it enables assessment and optimization of array performance. This is particularly important if the measurements are to be used for enforcement of noise regulations.Nominal microphone sensitivity and phase are manufacturerspecified, but the distribution (histogram) around these values is not. Hence, this work demonstrates a free-field comparison method for measuring these variations in a batch of arrays. We also provide the histograms at 1 kHz for a sample population of 8384 Knowles SPH0641LM4H-1 MEMS microphones (131 arrays of 64 microphones). The histograms follow t-distributions, resulting in 95% confidence intervals of ±0.39 dB for sensitivity and ±0.82 • for phase. Finally, we illustrate that delay-and-sum beamforming with these microphones results in a Gumbel-distributed gain with −0.13/+0.10 dB 95% confidence interval.

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Robust Bayesian Beamforming for Sources at Different Distances with Applications in Urban Monitoring

Cited by 2 publications

References 20 publications

Acoustic Beamforming Algorithms and Their Applications in Environmental Noise

Acoustic Beamforming Algorithms and Their Applications in Environmental Noise

Characterization of Mems Microphone Sensitivity and Phase Distributions with Applications in Array Processing

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