Performance analysis for microphone arrays with irregular geometries typically requires direct computation of beamforming gains over the spatial and frequency ranges of interest. However, theses computations can be very consuming and limit synthesis methods for applications that require rapid answers, as in the case of surveillance and mobile platforms. A better understanding of microphone arrangements and their impact on performance can result in more efficient objective functions for optimizing array performance. This article, therefore, analyzes the relationship between irregular microphone geometries and spatial filtering performance with Monte Carlo simulations. Novel geometry descriptors are developed to capture the properties of irregular microphone distributions showing their impact on array performance. Performance metrics are computed from three-dimensional beam patterns through a delay and sum beamformer with a fixed number of microphones for irregular arrays and comparable regular arrays. Statistical analysis and Multi-way Analysis of Variance establish relationships between key performance metrics and proposed geometry descriptors. It is demonstrated that in conjunction with array centroid offset and dispersion, statistics of the microphone differential path distance can explain variations of performance metrics when steering at targets for immersive or near-field microphone applications.
Bevel gear drive is widely used, quality of which not only affects its own transmission performance, size and weight, but also has some impact on the machine's performance. This paper introduces optimization design software for bevel gear, in which automatic optimization design is realized. In the paper mathematical model, programming of design data and realization of optimization design based on genetic algorithm are described in detail. The paper proposed integer serial number encoding genetic algorithm, which effectively deals with continuous and discrete variable optimization problem and reduces the code length of the string to improve the encoding and decoding efficiency, no invalid solution or duplicate solutions
Applications related to distributed microphone systems are typically initiated with sound source detection. This paper introduces a novel method for the automatic detection of sound sources in images created with steered response power (SRP) algorithms. The method exploits the near-symmetric coherent power noise distribution to estimate constant false-alarm rate (CFAR) thresholds. Analyses show that low-frequency source components degrade CFAR threshold performance due to increased nonsymmetry in the coherent power distribution. This degradation, however, can be offset by partial whitening or increasing differential path distances between the microphone pairs and the spatial locations of interest. Experimental recordings are used to assess CFAR performance subject to variations in source frequency content and partial whitening. Results for linear, perimeter, and planar microphone geometries demonstrate that experimental false-alarm probabilities for CFAR thresholds ranging from 10 −1 and 10 −6 are limited to within one order of magnitude when proper filtering, partial whitening, and noise model parameters are applied.
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