Coprime Microphone Array Reduces Hardware Complexity
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Solution Overview
Problem
Existing microphone array designs require a large number of microphones and significant computational resources to achieve accurate sound source localization, leading to high implementation costs and complexity.
Innovation Solution
The use of coprime microphone arrays, comprising pairs of uniform linear subarrays with coprime numbers of microphones, which significantly reduce the number of microphones and computational resources needed while maintaining performance, by utilizing a computing system to process signals and generate sound localization information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional densely populated microphone arrays are used to achieve accurate sound source localization, then measurement precision and array gain improve, but device complexity and implementation cost increase significantly
Solution Approach 1:
The microphone array is divided into multiple sparse subarrays, each with fewer microphones. These subarrays are arranged in a specific geometric configuration (e.g., co-prime spacing, nested structures) that collectively provides the necessary spatial sampling for accurate sound source localization, thereby reducing the total number of microphones while maintaining localization accuracy.
Solution Approach 2:
The patent transitions from conventional uniform linear arrays to multi-dimensional sparse array configurations. By introducing additional spatial dimensions and non-uniform spacing patterns (such as co-prime spacing between subarrays), the system achieves equivalent or superior localization performance with fewer microphones by exploiting higher-dimensional spatial information.
2Measurement precision
If the number of microphones in the array is increased to improve beam focusing and source localization, then direction-of-arrival estimation accuracy improves, but computational requirements increase
Solution Approach 1:
The signal processing is segmented into operations on individual sparse subarrays, which are then combined through covariance stacking or other fusion techniques. This segmentation reduces the computational burden by avoiding full-array processing and enabling parallel computation across subarrays, while maintaining DOA estimation accuracy through the collective information from all subarrays.
Solution Approach 2:
The patent changes the spatial sampling parameters by using co-prime or nested array configurations, which provide enhanced degree of freedom and improved Cramer-Rao bounds for DOA estimation. This parameter change allows achieving the same localization accuracy with fewer microphones, thereby reducing computational requirements for beamforming and spectral estimation algorithms.
3Reliability
If densely populated microphone arrays are deployed to achieve narrow beams for sound source identification, then beamforming performance improves, but implementation cost increases
Solution Approach 1:
The array is segmented into multiple sparse subarrays with specific geometric arrangements (co-prime spacing, nested configurations) that collectively provide the spatial sampling density needed for effective beamforming. This segmentation enables narrow beam formation and improved signal-to-noise ratio through coherent integration across subarrays, while reducing the total microphone count and associated implementation costs.
Data Source
AI summary
A coprime microphone array (CMA) system, comprising: a CMA arrangement that includes a pair of uniform linear microphone subarrays that are coincident and have a coprime number of microphones; a computing system that processing signals from each microphone in the CMA arrangement and generates sound localization information.