Optimization of Fixed Microphone Array in High Speed Train Noises Identification Based on Far-Field Acoustic Holography

Rujia Wang,Shaoyi Bei

doi:10.1155/2017/1894918

Abstract

Acoustical holography has been widely applied for noise sources location and sound field measurement. Performance of the microphones array directly determines the sound source recognition method. Therefore, research is very important to the performance of the microphone array, its array of applications, selection, and how to design instructive. In this paper, based on acoustic holography moving sound source identification theory, the optimization method is applied in design of the microphone array, we select the main side lobe ratio and the main lobe area as the optimization objective function and then put the optimization method use in the sound source identification based on holography, and finally we designed this paper to optimize microphone array and compare the original array of equally spaced array with optimization results; by analyzing the optimization results and objectives, we get that the array can be achieved which is optimized not only to reduce the microphone but also to change objective function results, while improving the far-field acoustic holography resolving effect. Validation experiments have showed that the optimization method is suitable for high speed trains sound source identification microphone array optimization.

Highlights

The noise of high speed vehicles such as high speed trains is one of the severest noise pollution sources [1, 2]
Based on studies in the reconstruction of the sound field microphone array, predecessors to build a grid array cross array have inherent defects; namely, in order to ensure a small main lobe width of the sound field reconstruction to improve resolution, the need to maintain a larger size of the array, such that the spacing between adjacent array elements, is increased and causes the emergence of grating lobes, which greatly weakened the ability of the sound field reconstruction array
The microphones array’s performance mainly reflected the result of spatial resolution and identification precision of source of noises; we used a microphone array of regular arrangement with equal distance as compared with an array of optimization displacement of microphones positions; in both simulations, the number of microphones is the same; but the size of the array may be different in the simulation result

Summary

Introduction

The noise of high speed vehicles such as high speed trains is one of the severest noise pollution sources [1, 2]. According to this principle, the sound field characteristic function reconstruction formula at any point s(ε, η) on the reconstruction side R is shown as follows: Ws (ε, η) t2. N θ where Ws(ε, η) is the sound field characteristic function for any point on the surface of the sound source s(ε, η) at time t within t1 − t2, P(t, ε, η) is the sound source estimated characteristic function applied of beamforming method, pi(t) is the received sound source pressure signals of ith microphone at time t, c is the sound velocity, N is the number of microphones, ri(t, ε, η) is the physical distance between the point s(ε, η) in the sound source surface and ith microphone at time t Based on this principle, across the entire surface of the sound source, sound field characteristic function of the distribution of the entire surface of the sound source can be obtained inside in time [t1, t2]. ⋅ [1 + (1 − 1 ) z0 ] e−jkr dx dy, jkr r r where M and N, respectively, are microphone rows and columns, H∗(m, n, f) is the mth row and the nth column of the holographic information of the microphone, r is the reconstruction surface points S(ε, η) mth row and the nth column from the microphone, and Δx and Δy are, respectively, microphone spacing and row distance

Compared Simulation

Validation Experiments

Conclusion