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1 

A COMPARISON OF BEAMFORMING PROCESSING TECHNIQUES FOR LOW 

FREQUENCY NOISE SOURCE IDENTIFICATION IN MINING EQUIPMENT
 

Hugo E. Camargo 

NIOSH/PRL 

Pittsburgh, PA, USA 

Patricio A. Ravetta 

AVEC, Inc.
 

Blacksburg, VA, USA 

Ricardo A. Burdisso 

AVEC, Inc.
 

Blacksburg, VA, USA 

Adam K. Smith 

NIOSH/PRL 

Pittsburgh, PA, USA 

ABSTRACT 

In an effort to reduce Noise Induced Hearing Loss (NIHL) in 

the mining industry, the National Institute for Occupational 

Safety and Health (NIOSH) is conducting research to develop 

noise controls for mining equipment whose operators exceed 

the Permissible Exposure Level (PEL). The process involves 

three steps: 1) Noise source identification (NSI), 2)

development of noise controls, and 3) evaluation of the 

developed noise controls. For the first and third steps, 

microphone phased array measurements are typically

conducted and data are processed using the conventional 

beamforming (CB) algorithm. However, due to the size and 

complexity of the machines, this task is not straight forward. 

Furthermore, because of the low frequency range of interest, 

i.e., 200 Hz to 1000 Hz, results obtained using CB may show 

poor resolution issues which result in inaccuracy in the noise 

source location. To overcome this resolution issue, two 

alternative approaches are explored in this paper, namely the 

CLEAN-SC algorithm and a variarion of an adaptive 

beamforming algorithm known as Robust Capon Beamformer 

(RCB). These algorithms were used along with the CB 

algorithm to process data collected from a horizontal Vibrating 

Screen (VS) machine used in coal preparation plants. Results 

with the array in the overhead position showed that despite the 

use of a large array, i.e., 3.5-meter diameter, the acoustic maps 

obtained using CB showed "hot spots" that covered various 

components, i.e., the screen deck, the side walls, the I-beam, 

the eccentric mechanisms, and the electric motor. Thus, it was 

not possible to identify which component was the dominant 

contributor to the sound radiated by the machine. The acoustic 

maps obtained using the RCB algorithm showed smaller "hot" 

spots that in general covered only one or two components. 

Nevertheless, the most dramatic reduction in "hot" spot size 

was obtained using the CLEAN-SC algorithm. This algorithm 

yielded acoustic maps with small and well localized "hot" spots 

that pinpointed dominant noise sources. However, because the 

CLEAN-SC algorithm yields small and localized "hot" spots, 

extra care needs to be used when aligning the acoustic maps 

with the actual pictures of the machine. In conclusion, use of 

the RCB and the CLEAN-SC algorithms in the low frequency 

range of interest helped pinpoint dominant noise sources which 

otherwise would be very hard to identify. 

INTRODUCTION 

Despite several efforts to improve the working 

environment in the mining industry, noise is perhaps the most 

challenging hazard to overcome. For that reason, occupational 

NIHL continues to be the most prevalent illness among mine 

workers. 

An analysis of audiograms for a large cohort of noiseexposed miners revealed that hearing impairment in this 

occupation increases exponentially with age. Consequently, 

49% of metal/nonmetal miners and 90% of coal miners have 

hearing impairment by age 50, in contrast to only 10% of 

workers not exposed to occupational noise1

. In this context, 

NIOSH is conducting research to reduce the sound radiated by 

mining equipment and thus minimize the exposure of miners to 

hazardous noise. The efforts of this research are geared towards 
2 

the loudest machines along the entire production chain, i.e., 

from the extraction at the face to the coal preparation plants. As 

a result, several noise controls have been developed for 

underground and surface equipment2-6. 

With regard to coal preparation plants, a cross sectional 

survey conducted by NIOSH determined that the vibrating 

screen (VS) machines are among the loudest noise sources used 

in these facilities, responsible for the worker noise exposures7

. 

Furthermore, the study revealed that 43.5% of coal preparation 

plant employees are exposed to noise levels that exceed the 

PEL8

. Analysis of the data reported to the Mine Safety and 

Health Administration (MSHA) by the mining industry showed 

that there are currently 421 coal preparation plants in 

production in the United States. 

The first step in the development of engineering noise 

controls involves the noise source identification (NSI) process, 

which consists of identifying the spatial location and the 

frequency content of the dominant noise sources. In the second 

step, appropriate (adequate) noise controls are developed. 

Then, the performance of these noise controls is determined by 

laboratory tests. Finally, the effectiveness of the controls in 

reducing the operator’s noise exposure is attained in situ. 

The first and third steps of the noise control development 

are achieved using microphone phased arrays. Usually, phased 

array data is processed using a CB algorithm which renders 

acoustic maps at each frequency of interest. 

Due to the massive nature of mining equipment, these 

machines are built using large and thick steel panels and thus 

radiate noise mostly at low frequencies, i.e., 200 Hz to 1000 

Hz. The challenge of using phased arrays for mining 

applications is then related to the frequency range of interest 

and the resolution obtained at such frequencies. 

There are two alternatives to overcome the low resolution 

issue. The first alternative consists of increasing the array 

aperture, i.e., the size of the array. However, there are several 

physical constraints that limit this alternative such as the 

dimensions and shape of the machines, and the distance from 

the array to the device under test. The second alternative 

consists of using advanced beamforming algorithms that 

improve the resolution of the acoustic maps at low frequencies. 

Previous work on noise source identification on a vibrating 

screen involved the use of two different arrays: A 1.98-meter 

diameter array provided with 49 microphones, and a 3.5-meter 

diameter array equipped with 121 microphones. When the latter 

array was used, the resolution of the acoustic maps in the 200 

Hz – 1000 Hz frequency range was improved by approximately 

50%. However, at some frequencies, it was still not clear which 

components were responsible for most of the sound being 

radiated. 

This paper explores two advanced beamforming tools to 

improve the resolution of the acoustic maps at low frequencies: 

the Robust Capon Beamformer (RCB) and the CLEAN-SC 

algorithm. 

NOMENCLATURE 

b x(

r

b ) is the beamforming output. 

C x

r

(

r

) is the array propagation vector for a point at x 

r

G is the cross-spectral matrix of the microphone signals. r r rj is the distance from point xb to microphone j.

r r

w x( ) b is the microphone weight vector. 

x

r

b is the position of a point in the sound field. 

x

r

j is the position of microphone j.

CONVENTIONAL BEAMFORMING 

Conventional beamforming is based on the delay-and-sum 

concept. In this approach, the signal of each microphone in the 

array is delayed by the propagation time from a point in space. 

The signals are then added to obtain the resulting noise. If a 

source is present, the signals add constructively and a large 

output is obtained. If not, the signals add destructively and thus 

yielding a smaller output. Since the location of the source is not 

known a priori, a set of scanning points must be tested to 

determine if a source is present. 

For stationary sources it is common to work in the 

frequency domain9 where a time delay is equivalent to a phase 

shift and conventional beamforming takes the following form10, 

bx (

r r r r b ) = w*

() x b G w() x b (1) 

In equation (1), the weight vectors w(xb ) are used to “steer” 

the array towards xb and obtain the beamforming output 

b(xb ) , representing the sum over the array microphones of the 

acoustic pressure induced by the source located at xb . The

weight vectors are usually defined using the array propagation 

vector as, 

r r r r C x( ) w x( ) = b

b r r (2) C x( ) b
C x

r where the components of the propagation vector (

r

b ) are 

the free-space Green’s function given by, 

r r

e 

ik x 

e C x( ) r − b −x j −ikr j

j b = r r = (3) 4π x b − x j 4πrj

r Ideally, if a point source exists at xb , the beamforming 

output would be the mean squared value of the pressure at the 

array due to such source and it would be zero if there is not a r source at xb . However, the fact that there is a finite number of 

microphones in the array results in the presence of sidelobes 

(i.e. lobes not associated to actual sources) in the acoustic 

maps. The resolution of the array at a particular frequency is 

defined as the size of the region for which the levels are within 

3 dB of the peak value in the mainlobe. For conventional 

beamforming, the resolution is directly proportional to the 

wavelength. 

ROBUST CAPON BEAMFORMING 

This technique is a variation of the Capon beamformer11 

that accounts for uncertainties in the steering vector by using an 

ellipsoidal uncertainty set. Different implementations of the 

approach were developed by Li, Stoica and Wang12-14. This 

approach falls in the category of diagonal loading. Diagonal 

loading also improves robustness. This approach is dataadaptive, i.e. the algorithm uses the data to improve the results, 

making it less sensitive to errors in the steering vector. 

As a result, array resolution and signal-to-noise ratio are 

improved. The Robust Capon Beamformer approach has also 

been shown to be more computationally efficient than other 

Capon beamformers12. 

CLEAN-SC 

This method developed by Sijtsma is based on the CLEAN 

algorithm used for astronomy developed by Högbom15. 

Sijtsma’s work extends CLEAN to account for spatial source 

coherence16. This iterative approach basically removes the 

contribution of sources that are spatially coherent with a major 

source. This is accomplished by taking advantage of the fact 

that sources are spatially coherent with their corresponding 

sidelobes. 

The iteration goes as follows: 1) remove the highest source 

in the map (and its spatially correlated sidelobes) from the 

cross spectral matrix, 2) remove the part of the map associated 

with the source removed, i.e., its point spread function, and 3) 

add the source to a new “clean” map. This process is repeated 

until the energy in the cross-spectral matrix is similar to the 

energy of the last source removed. 

As a result, the new map only shows the actual noise 

sources and not their sidelobes. Since the approach uses a 

single point in the grid as the source location, the new map is 

created using a user defined array resolution, i.e. a beam of an 

arbitrary diameter in which the source level drops 3 dB from its 

center. 

EXPERIMENTAL SETUP 

Phased array measurements were conducted in the Hemianechoic chamber of the PRL. This chamber is 16.8-meter long 

by 10.1-meter wide, by 6.4-meter high. The walls and ceiling 

are treated with acoustic absorptive material, i.e. fiberglass, 

resulting in a chamber cut-off frequency of 100 Hz according 

to ISO 374417. 

The device under test was a horizontal dewatering VS 

machine used in coal preparation plants. The VS machine 

consists of a boxed structure that is suspended on coil springs. 

Figure 1 shows a schematic of the VS machine and the phased 

array in the Hemi-anechoic chamber. 

Figure 1. Schematic of the VS and the phased array in the 

Hemi-anechoic chamber.
 

The VS machine is driven by two eccentric mechanisms 

that set the structure into vibration at a 45-degree angle with 

respect to the vertical axis (z-axis). 

The excitation for the noise radiated by the screen body is 

mainly provided by the eccentric mechanisms and the gears 

used to drive them. Each eccentric mechanism uses two 

rotating unbalanced shafts. One shaft of the first eccentric 

mechanism is driven by an electric motor via a belt/pulley 

system. This shaft is directly coupled to one of the shafts in the 

second eccentric mechanism. The second shaft in each 

eccentric mechanism is driven by the main shaft through a gear 

set. Figure 2 displays a top view of the VS machine where all 

the various components are shown. 

3 

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distribution is unlimited. 
4 

Left Mechanisms I-beam

Screen panels Electric motor

Right 

Mechanisms 

Figure 2. VS machine as seen from the top. 

A 121-channel, 3.5-m diameter microphone phased array 

designed and built by AVEC, Inc. was used for this series of 

measurements. This array is an 11-arm star array with a 

proprietary microphone arrangement. In order to provide the 

most complete data set for noise source identification, the array 

was mounted to a movable truss, i.e., gantry crane, at the 

NIOSH facility. It was mounted in two configurations: vertical 

(microphone plane parallel to the walls) and horizontal 

(microphone plane parallel to the ground). Figure 3 shows the 

VS machine in the hemi-anechoic chamber with the array in the 

overhead (horizontal) position. 

Microphone Phased Array 

Suspension Springs 

Figure 3. VS machine in the Hemi-anechoic chamber at PRL 

with the AVEC phased array in the overhead position. 

Data was collected with the array centered at three 

different places above the vibrating screen (horizontal 

configuration), and then from each side of the machine (vertical 

configuration). This paper includes results for the array 

centered on top of the vibrating screen. 

The data acquisition system that was used for these 

measurements is a 128-channel simultaneous acquisition 

system with signal conditioning and anti-aliasing filtering also 

built by AVEC. The sampling frequency used was 51.2 kHz. 

For each configuration, 16 seconds of continuous data were 

collected. Processing was performed in 1/3rd octave bands. 

Conventional beamforming results were obtained using 

AVEC’s phased array software. Results for CLEAN-SC and 

RCB were obtained with non-commercial implementations of 

such algorithms developed by AVEC for research purposes. 

RESULTS 

As shown in figures 4 through 6, there are significant 

differences in the acoustic maps. Note that the location of the 

sources obtained with CLEAN-SC agrees to those found using 

CB. This is related to the procedure to find noise sources. Thus, 

the maximum source on each map is “removed” from the crossspectral matrix and a source with such level is located on the 

“clean” map. As explained before, this process is repeated until 

the energy in the cross spectral matrix is less than the energy 

“recovered” for the last source. Keep in mind that in this 

approach, the resolution is arbitrarily set by the user, and hence 

the extraordinary resolution of the technique. It was precisely 

this extraordinary resolution on the acoustic maps that allowed 

pinpointing dominant noise sources on the various components 

of the VS machine. The fact that in some cases sources that are 

not well defined (i.e. not circles) are “condensed” to a single 

point is due to the ability of CLEAN-SC to remove all the 

power from the cross-spectral matrix that is correlated with the 

source identified in each step. The map resulting after 

extracting all the major noise sources has not been plotted in 

these figures since we were only interested in the major noise 

sources. 

However, the fact that some noise sources are “condensed” 

to a single point requires that extra care should be practiced 

when aligning the acoustic map with the picture of the device 

under test, i.e. the VS machine in this case. Consider for 

instance Figure 5c that shows a dominant noise source in the 

left eccentric mechanism. If the picture is not properly aligned, 

i.e. suppose the picture is off to the left by a couple of inches, 

then the results would indicate that the noise source is located 

in the I-beam. This would lead to an erroneous NSI process. 

At low frequencies, RCB improves the resolution allowing 

for more accurate noise source identification. However, note 

that results obtained with RCB show that some of the sources 

“disappear”, especially at higher frequencies, as is the case 

when comparing figure 6a and 6b. This suggests that some of 

the sources in the CB maps are actually sidelobes. 
(a) 

(b) 

(c) 

Figure 4. Comparison of acoustic maps at 250 Hz obtained 

with: a) CB, b) RCB, and c) CLEAN-SC. 

(a) 

(b) 

(c) 

Figure 5. Comparison of acoustic maps at 315 Hz obtained 

with: a) CB, b) RCB, and c) CLEAN-SC. 

These results are confirmed by analyzing the CLEAN-SC 

results, in which some of the hot spots relative levels have 

changed dramatically, i.e., the hot spots are coherent with each 

other and hence removed by CLEAN-SC when the 

corresponding main source is subtracted from the map (see 

Figure 6). 

It should be noted that for this effort, a parametric study of 

the effect of the parameter (ε) on the RCB results was not 

conducted. This parameter is user defined and it describes the 

uncertainty in the steering vector. A conservative approach was 

used by applying the parameter value ε=2 suggested in the 

literature14. 

5 

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distribution is unlimited. 
(a) 

(b) 

(c) 

Figure 6. Comparison of acoustic maps at 400 Hz obtained 

with: a) CB, b) RCB, and c) CLEAN-SC. 

CONCLUSION 

The implementation of an adaptive beamforming algorithm 

(RCB) and a post-processing technique (CLEAN-SC) on the 

results obtained from a VS machine using a 3.5-meter array 

were studied as a mean to improve the resolution and thus the 

accuracy of the NSI process at low frequencies, i.e., from 200 

Hz to 1000 Hz. Both methods have shown significant 

improvements when compared to the results obtained using 

conventional beamforming in this frequency range. 

Furthermore, it was the use of these tools that allowed precise 

and rapid pinpointing of dominant noise sources on the various 

components of the VS machine. An alternative approach such 

as intensity mapping would be inpractical due to the 

dimensions and complexity of the machine. 

The results obtained at PRL with both the RCB and the 

CLEAN-SC methods will be used to develop noise controls to 

attenuate the sound radiated by the VS machine, and thus 

achieve the ultimate goal of reducing the noise exposure of coal 

preparation plant employees. 

ACKNOWLEDGMENTS 

The authors would like to thank Jian Li and Peter Stoika 

for their permission to publish the results obtained using RCB. 

Thanks are also due to Peter Kovalchik, Pat McElhinney, Lynn 

Alcorn, Dave Yantek, and Nicolas Macko for their help and 

support during the test. 

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6 

This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; 

distribution is unlimited. 
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7 

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distribution is unlimited.