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Research Papers

Ghost Image Suppression Based on Particle Swarm Optimization-MVDR in Sound Field Reconstruction

[+] Author and Article Information
Min Li

Research Center for Aerospace
Vehicles Technology;
School of Mechanical Engineering,
University of Science and Technology Beijing,
Beijing 100083, China
e-mail: limin@ustb.edu.cn

Long Wei

Research Center for Aerospace
Vehicles Technology;
School of Mechanical Engineering,
University of Science and Technology Beijing,
Beijing 100083, China
e-mail: number5wei@126.com

Qiang Fu

Research Center for Aerospace
Vehicles Technology;
School of Mechanical Engineering,
University of Science and Technology Beijing,
Beijing 100083, China
e-mail: zealpaladinf@gmail.com

Debin Yang

Research Center for Aerospace
Vehicles Technology;
School of Mechanical Engineering,
University of Science and Technology Beijing,
Beijing 100083, China
e-mail: ydb@ustb.edu.cn

1Corresponding author.

Contributed by the Noise Control and Acoustics Division of ASME for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received November 16, 2013; final manuscript received November 13, 2014; published online January 27, 2015. Assoc. Editor: Lonny Thompson.

J. Vib. Acoust 137(3), 031007 (Jun 01, 2015) (13 pages) Paper No: VIB-13-1402; doi: 10.1115/1.4029166 History: Received November 16, 2013; Revised November 13, 2014; Online January 27, 2015

In sound field reconstruction, spurious sources called ghost images always appear around the position of the real sound source in the sound pressure distribution map because of the grating and side lobes, thus resulting in an incorrect identification of the sound source. To solve this problem, a method for suppressing ghost images is proposed in this paper; such method is based on particle swarm optimization (PSO) and minimum variance distortionless response (MVDR) beamforming. In this method, the elements distribution of a microphone array is first optimized by the PSO algorithm to acquire the optimal design of an unequal spacing microphone array. With this array, the grating lobe is suppressed, and the increscent value of the inherent side lobe value is reduced. Second, MVDR algorithm is used to weaken the effect of the side lobes and to obtain a sound pressure distribution map in which the ghost images are suppressed. The advantage of this method is the combination of the unequal spacing array, which suppresses the grating lobe, and the MVDR algorithm, which has excellent performance in spatial filtering. Through this method, a microphone array with a few number of elements can achieve ghost image suppression. Experiments on sound field reconstruction in an anechoic chamber for a single-tone sound source are conducted to validate the proposed method. Moreover, some extra sound field reconstructions for a single-tone sound source and double sound sources with broadband in a normal room with different parameters such as the array shape and distance from the sources to the array are conducted to discuss their influences on the effectiveness of the proposed method.

Copyright © 2015 by ASME
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Figures

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Fig. 1

The model of receiving the sound signal by a microphone array

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Fig. 2

The initial coordinate of each element

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Fig. 3

The convergence results with different quantities of particles

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Fig. 4

The elements distribution of the optimal array

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Fig. 5

The hardware layout and environment of the experiment. (a) The layouts of the array and the sound source and (b) the anechoic chamber.

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Fig. 6

Sound pressure distribution maps in the anechoic chamber by das algorithm in 2000 Hz. (a) With uniform array and (b) with the optimal cross unequal spacing array.

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Fig. 7

Sound pressure distribution maps in the anechoic chamber by das algorithm in 4000 Hz. (a) With uniform array and (b) with the optimal cross unequal spacing array.

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Fig. 8

Sound pressure distribution maps in the anechoic chamber by das algorithm in 6000 Hz. (a) With uniform array and (b) with the optimal cross unequal spacing array.

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Fig. 9

Sound pressure distribution maps in the anechoic chamber by das algorithm in 8000 Hz. (a) With uniform array and (b) with the optimal cross unequal spacing array.

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Fig. 10

Sound pressure distribution maps in the anechoic chamber with unequal spacing array in 2000 Hz. (a) DAS algorithm and (b) MVDR algorithm.

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Fig. 11

Sound pressure distribution maps in the anechoic chamber with unequal spacing array in 4000 Hz. (a) DAS algorithm and (b) MVDR algorithm.

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Fig. 12

Sound pressure distribution maps in the anechoic chamber with unequal spacing array in 6000 Hz. (a) DAS algorithm and (b) MVDR algorithm.

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Fig. 13

Sound pressure distribution maps in the anechoic chamber with unequal spacing array in 8000 Hz. (a) DAS algorithm and (b) MVDR algorithm.

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Fig. 14

The elements distribution of the optimal random Archimedes spiral array

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Fig. 15

Sound pressure distribution maps with the optimal cross array in the normal room. (a) f = 2000 Hz, (b) f = 4000 Hz, (c) f = 6000 Hz, and (d) f = 8000 Hz.

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Fig. 16

Sound pressure distribution maps with optimal Archimedes spiral array in the normal room. (a) f = 2000 Hz, (b) f = 4000 Hz, (c) f = 6000 Hz, and (d) f = 8000 Hz.

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Fig. 17

The layouts of the array and the sound source

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Fig. 18

Sound pressure distribution maps for double sound sources in the normal room. (a) 500–2000 Hz, (b) 2000–4000 Hz, (c) 4000–6000 Hz, and (d) 6000–8000 Hz.

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Fig. 19

Sound pressure distribution maps for the frequency band 500 Hz–750 Hz. (a) L0 = 0.5 m, (b) L = 0.6 m, and (c) L = 0.7 m.

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Fig. 20

Sound pressure distribution maps for the frequency band 750 Hz–1000 Hz. (a) L0 = 0.7 m, (b) L = 0.8 m, and (c) L = 0.9 m.

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Fig. 21

Sound pressure distribution maps for the frequency band 1000 Hz–1250 Hz. (a) L0 = 0.9 m, (b) L = 1.0 m, and (c) L = 1.1 m.

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Fig. 22

Sound pressure distribution maps for the frequency band 1250 Hz–1500 Hz. (a) L0 = 1.1 m, (b) L = 1.2 m, and (c) L = 1.3 m.

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Fig. 23

Sound pressure distribution maps for the frequency band 1500 Hz–1750 Hz. (a) L0 = 1.3 m, (b) L = 1.4 m, and (c) L = 1.5 m.

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Fig. 24

Sound pressure distribution maps for the frequency band 1750 Hz–2000 Hz. (a) L0 = 1.6 m, (b) L = 1.7 m, and (c) L = 1.8 m.

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Fig. 25

Values of L0 in different frequency bands

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