Abstract
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.
Highlights
Microphone arrays use spatial diversity of element positions to capture acoustic signals and reduce degradation brought on by reverberation and noise
In order to resolve between classes of irregular arrays, the following paragraphs analyze geometry descriptors based on differential path distance (DPD) statistics with fixed centroids and dispersions, and demonstrate their ability to identify classes of irregular geometries with similar performance properties
5 Conclusions This article analyzes and identifies important characteristics for irregular microphone arrays that directly related to beamforming performance
Summary
Microphone arrays use spatial diversity of element positions to capture acoustic signals and reduce degradation brought on by reverberation and noise. It is widely applied in speech enhancement, teleconferencing, talker tracking, hands-free human-machine interfaces, and acoustic surveillance systems [1,2]. Because most of these applications involve separating desired signals from noise and estimating acoustic parameters, array performance is usually assessed by its ability to locate, track, and separate sound sources in the field of view (FOV) [1]. Classes of irregular geometries for immersive environments are statistically analyzed through Monte Carlo simulations to identify key geometric characteristics related to array performance
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