Applied Acoustics 74 (2013) 364–374

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Applied Acoustics

journal homepage: www.elsevier .com/locate /apacoust

An empirical technique for predicting noise exposure level in the typicalembroidery workrooms using artificial neural networks

Mohsen Aliabadi a, Rostam Golmohammadi b,⇑, Muharram Mansoorizadeh c, Hassan Khotanlou c,Abdoreza Ohadi Hamadani d

a Department of Occupational Hygiene, Faculty of Public Health, Hamadan University of Medical Sciences, Hamadan, Iranb Department of Occupational Hygiene, Faculty of Public Health and Center for Health Researches, Hamadan University of Medical Sciences, P.O. Box 4171-65175, Hamadan, Iranc Department of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamadan, Irand Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran

a r t i c l e i n f o

Article history:Received 30 June 2012Received in revised form 15 August 2012Accepted 23 August 2012Available online 24 October 2012

Keywords:Noise predictionNeural networksIndustrial embroideryEmpirical technique

0003-682X/$ - see front matter � 2012 Elsevier Ltd.http://dx.doi.org/10.1016/j.apacoust.2012.08.009

⇑ Corresponding author. Tel.: +98 811 8380090; faxE-mail addresses: mohsen.aliabadi@umsha.ac.ir (

umsha.ac.ir, Gol1965@yahoo.com (R. Golmohamm(M. Mansoorizadeh), hkh@basu.ac.ir (H. Khotanlou),Hamadani).

a b s t r a c t

Noise prediction is an important aspect of noise control in the design phase and the utilization phase ofthe industrial processes. This is of particular importance in the industrial embroidery which is an impor-tant part of the textile industry and in which workers are exposed to excessive noise. Using artificial neu-ral networks, this study aims to present an empirical technique for predicting the noise level in thetypical embroidery processes. The data from nine acoustic, structural and embroidery process featuresthat influence the noise in 60 workrooms was used to develop the noise prediction technique. Multilayerfeed forward neural networks with different structures were developed by using MATLAB software andgenetic algorithm was employed to determine the optimal value for the initial weights of neural net-works. Moreover, multiple regression techniques were employed and their results were compared withthose of neural networks. The results showed that the neural networks provided more accurate predic-tions than did multiple regression techniques. The best neural networks could accurately predict thenoise level (RMSE = 0.69 dB and R2 = 0.88). Our results demonstrate that, the developed empirical tech-nique can be a helpful tool to analyze the noise pollution in the mentioned process and can enable acous-tics and occupational health professionals to apply hearing conservation programs.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Noise is considered to be the most persistent physical contam-inant in industrial workplaces for workers to expose [1]. In thedeveloping countries where, compared with developed countries,modern technology of designing, implementing and utilizingindustrial processes is not easily accessible, industrial noise pollu-tion is of great importance [2,3]. Long exposure to excessive noisecan cause permanent hearing loss, which finally results in the dis-turbance in verbal communication and in the quality of socialbehaviors [4]. In addition, exposure to high noise level contributesto undesirable physiologic effects such as hypertension and mentaldisorders such as discomfort and noise annoyance [5,6]. On theother hand, the mentioned health effects, which are all caused bythe exposure to noise can affect job performance and workers’ pro-ductivity [7]. Regarding the legal responsibility of industrial plants

All rights reserved.

: +98 811 8380509.M. Aliabadi), golmohamadi@

adi), mansoorm@basu.ac.ira_r_ohadi@aut.ac.ir (A. Ohadi

and the growth of public awareness about the outcomes of thenoise, there is now an inclination among industrial managers to re-duce the noise exposure level in workroom which has led to theexecution of hearing conservation program, as the most importantmethod of preventing noise-induced hearing loss. The basic com-ponent of this program is predicting and determining the level ofnoise exposure in noisy workrooms [8–10]. Predicting the noise le-vel in workplaces is regarded as an important aspect of the noiseengineering control [11]. Prediction of the noise induced by indus-trial machinery and respected processes especially in the feasibilitydesign and establishment phases of industrial process can helpconsider the occupational noise exposure limits, which will bemostly economical [11]. Prediction of the noise level in enclosedspaces, especially in industrial buildings is the first priority ofactivity design. That is because conducting efficient noise controlprograms after the design and establishment phases and withinthe utilization stage has special limitations, and usually imposesgreat cost and low efficacy [12,13]. In addition, prediction of noiselevel in industrial processes which are being utilized helps assessthe pre- and post-prevention control measures in terms of cost-benefit and efficacy [14].

M. Aliabadi et al. / Applied Acoustics 74 (2013) 364–374 365

Physical parameters that influence the noise wave propagation,due to the complexity and large number, can produce some prob-lems for the development of noise prediction models [15]. In orderto predict the noise level in enclosed workrooms, researchers haveproposed different models, some of which include simple theoret-ical equations, some are empirical models and some employ com-plex methods such as ray tracing [16].

The classic model of noise prediction in closed workrooms titleddiffuse field theory predicts the distribution of sound pressure lev-els caused by a point source regarding a certain number of acousticvariables and noise power level of source. The application domainof this model which was proposed by Sabin and Eyring is in theworkrooms that are completely diffuse sound field. In real situa-tions, due to the irregular shapes of workrooms and because ofthe workroom surfaces being set up to different sound absorptioncoefficients, this theory does not hold [17]. Most of the developedstatistical and empirical models of predicting noise in the indus-trial workrooms are based on the certain distance between anomnidirectional point sound source and receiver’s position. Theanalysis of some empirical models developed after the diffuse fieldtheory showed the low validity and the limited application domain[12,18]. Using statistical techniques and accounting for new fea-tures such as fitting density, have increased the validity and theapplicability of newer empirical models of noise prediction inindustrial workrooms [16]. Note that, these empirical predictionmodels aim to predict the pattern of sound propagation in differentacoustic conditions of enclosed spaces as a spatial sound distribu-tion curve [19]. More recent prediction models are geometricalones which present higher validity and acceptability in comparisonwith the empirical models [20,21]. The development of these mod-els is complex and requires computer and acoustics expertise andtime-consuming calculations so that they are used in such specialcases as in auditoriums in which the quality of sound propagationis of great importance. Ray tracing model proposed by Barby andQndet is the most important model of this type [22–24]. Numerousfeatures with nonlinear relationships can influence noise propaga-tion in real enclosed spaces. Therefore, due to the non uniform pat-tern of sound propagation, the classical statistical methods cannotcompletely explain these relationships. Because of the wave natureof sound and the diffusion complexity in enclosed workrooms,empirical models which possess high validity and applicabilityare rarely found [19]. In this regards, one of the most important is-sues is applying alternative methods which can recognize the com-plex relationships among the different variables which affect thenoise level in enclosed workrooms. Artificial neural networks(ANNs) as one of the most important artificial intelligence tech-niques have innate talent to store and to apply empirical data. Thistechnique is known to be an accurate approach in modeling vari-ous phenomena in basic, engineering and medical sciences withthe ability to determine the complex relations among differentvariables and the capacity of using multiple learning algorithms[25]. Neural networks can be a good tool for analysis of such phys-ical phenomena as sound in which adequate data related to differ-ent variables are being collected while the mechanisms ofinteraction effects are complex and not fully understandable [26].

Despite the increasing use of neural networks as a new ap-proach to predicting acoustic performance of absorbents, activenoise control and environmental acoustics, few studies aboutacoustic parameters have used such techniques in enclosed spaces[27–31]. Employing neural networks to predict the sound speechlevel in the classrooms and the reverberation time (as the mostimportant acoustics parameter) in the auditoriums are two exam-ples in this regard [32,33]. Therefore, it is necessary to developmore applied technique which can accurately predict the noise le-vel in workrooms and also can take the features of real noisesources and important acoustics variables. The noisy process of

industrial embroidery in which patterns and designs are imprintedon cloth, is considered to be an important part of the textile indus-try [34]. Using artificial neural networks, this study aims to presentan empirical technique for predicting noise exposure level in en-closed spaces of industrial embroidery process. In addition, multi-ple regression techniques were also employed and their resultswere compared with those of neural networks.

2. Materials and methods

In this study, the development of the prediction technique in-cluded in three main phases based on artificial neural networksalgorithm. The first phase consisted of choosing the suitable re-search field in terms of noise pollution, identifying probable vari-ables influencing the noise level of industrial process, collectingand analyzing data set related to variables, determining the mainvariables influencing the noise level, normalizing the data and fi-nally determining the biased observation by statistical techniques.The second phase consisted of separating the data into train andtest data sets, determining the type and the parameters of ANNsand learning algorithm and applying networks training. In thethird phase, the test data set was employed in order to assess thevalidity of the best developed technique in terms of performancecriteria.

2.1. Source of data

The structure and the performance of ANNs as a predictionmodel depend upon the complexity of the issue and the numberof selected features along with valid data set of features [25]. Thedata were collected from 60 workrooms located in the Khorasanprovince, East of Iran. Most of modern embroidery processes arecontrolled by computer and specialized software. Multi-needledmachinery has some heads, each of which sews a similar patternon the separated cloth concurrently. The operators in the work-rooms plan and monitor the machinery performance, cloth andstring characteristics and the patterns. Due to the nature of sewingoperations, operators are exposed to noise as a pollutant with somerisks of hearing loss. Moreover, similar nature and structure ofembroidery processes make it possible to restrict and control thenumber of variables that influence the development of noise pre-diction technique.

2.2. Characterizing features

2.2.1. Acoustical, structural features of workroomsThe most important structural characteristics were dimensions

of length (L), width (W), height (H), total surface area (S) and geo-metrical shape of workrooms. Because the geometrical shapes ofall workrooms were similar (all rectangular), the parameters offloor, ceiling, walls area along with the area of all surfaces and vol-ume of workrooms were selected.

Determining the acoustical features were based on ISO 11690-3[13]. The structural materials were recorded carefully and theirsound absorption coefficient values were specified from valid re-sources [35,36]. As the surface absorption coefficient is a functionof the frequency of the incidence sound, noise reduction coefficient(NRC), which is defined as the average values of sound absorptioncoefficient in the 250 Hz, 500 Hz, 1000 Hz and 2000 Hz octavebands was used [35]. Average absorption coefficient of workroom(�a), equivalent absorption surface area (A) in m2 units and roomabsorption constant (R) in m2 units as important acoustic featureswere determined based on Sabin’s theory as follows [35].

�a ¼P

ajSj

S0ð1Þ

366 M. Aliabadi et al. / Applied Acoustics 74 (2013) 364–374

A ¼ S0 �aðm2 �MKS sabinsÞ ð2Þ

R ¼P

�aS0

1� �að3Þ

As one of the most acoustic features, reverberation time (RT) insecond was calculated based on Sabin’s theory as follows [35].

RT ¼ 0:16� VS0 �a

ð4Þ

where S0 is total surface area of each of workroom and V is volumeof each of workroom. Sabin’s formula for calculating acoustic fea-tures can be applied in the closed spaces with regular shapes andrelatively low average absorption coefficients. This formula wasused due to its simplicity and ease of application [37–40]. In addi-tion, this study aimed to use acoustical and structural features todevelop a noise prediction technique so that the applied aspectsof the technique are facilitated [23]. Descriptive statistics of struc-tural and acoustical features of workrooms were listed in Table 1.

2.2.2. Features of embroidery processSome features of embroidery machinery, were recognized to be

important from the viewpoint of noise propagation in each work-room, including the type of machinery maker (MT), the numberof embroideries (NE), the number of machinery heads (NH), thelifetime of embroidery (EL) and the operation speed of embroidery(ES) in stitches per minute. The makers of embroidery machinerywere divided into two types based on the manufacturing company.It is noted that the geometrical shape of setting and installing themachinery was designed in terms of machinery dimension equallybased on the workroom length. Moreover, the most important fea-tures of consumed materials, which could influence the noise levelof embroidery operations, were the type of fabric (FT) and string(ST) used in the embroidery. The fabrics used were classified interms of thickness decreasing in seven major classes, includingthree layered fabric, felted, duplex cotton, lee, monoplex cotton, sa-tin and silk, based on ordinal scale. The embroidery string was clas-sified into polyester, silk, braid and cotton, based on nominal scale.Descriptive statistics of embroidery process features of workroomswere listed in Table 1.

Table 1Descriptive statistics of candidate features of embroidery workrooms for developing noise

Features type Symbol Unit

Workroom constructional featuresLength L mWidth W mHeight H mVolume V m3

Total surface S m2

Workroom acoustic featuresAverage absorption coefficient �a –Equivalent absorption surface A m2

Room absorption constant R m2

Reverberation time RT s

Process featuresNumber of embroideries NE –Number of embroidery heads NH –Embroidery lifetime EL YearEmbroidery speed ES Stitches/minFabric typea FT –String typea ST –Machine maker typeb MT –

Target featureNoise equivalent level LAeq dB

a This feature was based on ordinal scale.b This features were based on nominal scale.

2.2.3. Feature of noise levelThe noise pressure level in the workrooms under study was

considered as a target variable in the prediction technique. Mea-suring equivalent noise pressure level (LAeq) in workstations wasperformed based on ISO 9612 and ISO 11200 [41,42] by using inte-grated sound level meter Norsonic type132. LAeq is the equivalentsteady noise level of a noise energy-averaged over time. Becauseof the fact that noise is often a complex fluctuation, the noise levelhas to be averaged over a minimum sample time. The samplingtime can be as short as a few minutes if the noise level is steadystate. Therefore, the measurement time was selected to be10 min. Regarding the length dimension of the machinery, threemeasurement points were considered as grid area in similardimensions based on operators’ walking path around the machin-ery. To measure noise, microphone of the sound level meter waslocated within the vicinity of workers’ hearing zone. Logarithmicaverage of noise levels in reference points was calculated and re-corded as noise exposure level. The result of the pilot study showedthat the variations of noise level around the each machinery wereinconsiderable due to similar nature of all operation points ofmachinery. Despite the consistency of structural and acousticalfeatures of each workroom, through changing the characteristicsof embroidery process and re-measuring the noise level somenew observations in the workrooms appeared. Therefore, thenumber of recorded observations was increased up to 100.Descriptive statistics of noise exposure level in workrooms werelisted in Table 1.

2.2.4. Selecting final featuresTo process and select the final features of the noise prediction

technique, statistical methods such as correlation matrix wereused. In correlation matrix, according to Table 2, having specifiedthe input features which had significant correlation with outputfeature, we removed less important features. In addition, the inputfeatures of the correlation matrix which had higher correlationwith each other were analyzed with the less important featuresbeing removed. Finally, nine features including the type of machin-ery maker (MT), the number of embroideries (NEs), the number ofmachinery heads (NHs), the operation speed of embroidery (ES),the lifetime of embroidery (EL), the type of fabric (FT), the average

prediction technique.

Minimum Maximum Mean Std. deviation

5.70 20 10.40 4.052.70 11 4.94 1.842 5 3.58 0.7152.50 660 188 129.2589.40 640 207 119.40

0.04 0.14 0.06 0.023.92 25.07 11.44 5.574.10 26.09 12.25 6.110.94 4.21 2.70 0.89

1 4 1.24 0.536 40 14.31 5.701 20 11.40 5.34690 850 747 39.241 7 – –1 4 – –1 2 – –

79.40 88.70 85.20 1.96

Table 2Correlation matrix of the final selected features for developing noise prediction technique.

MT NE NH EL ES FT RT �a R LAeq

MT 1NE �0.059 1NH �0.090 0.844a 1EL �0.529a 0.056 0.176 1ES �0.096 0.287a 0.471a 0.169 1FT �0.042 0.209b 0.292a 0.047 0.317a 1RT �0.151 0.111 0.199b 0.255b 0.138 0.066 1�a 0.226b �0.002 �0.166 �0.282a �0.212b �0.115 �0.840a 1R 0.203b 0.131 0.013 �0.109 �0.097 �0.103 �0.553a 0.794a 1LAeq �0.373a 0.333a 0.535a 0.407a 0.654a 0.462a 0.314a �0.416a �0.371a 1

a Correlation is significant at the 0.01 level (2-tailed).b Correlation is significant at the 0.05 level (2-tailed).

M. Aliabadi et al. / Applied Acoustics 74 (2013) 364–374 367

absorption coefficient (�a), the room absorption constant (R) andthe reverberation time (RT) of workroom were determined as finalinput features to develop the noise prediction techniques.

2.3. Neural networks construction

An ANNs consists of a number of interconnected neurons whichare positioned in at least three layers, i.e. one input layer of sourceneurons, at least one hidden layer and an output layer of computa-tional neurons. Each neuron in the hidden layer of the network cangenerally be considered as a simple processing element taking oneor more input(s) and giving one and more output(s). In each neu-ron, a related weight is assigned to an input through which thepower of each input is adjusted. The neuron can, thereby, combineall the inputs and calculate an output, which is passed on. One caneasily propose the basic equations to express the structure of typ-ical ANNs as follows [25].

yi ¼ FðziÞ ð5Þ

zi ¼Xn

i¼1

wixi þ bi ð6Þ

Xn

i¼1

wixi ¼ w1x1 þw2x2 þw3x3 þ � � � þwnxn ð7Þ

where x1, x2, and xn denote the measured value for input variables,w1, w2, and wn are the weights, bi is the bias, yi is output variablewhile Zi could be considered as any used activation function. Non-linearity can be considered as the main activation function of neuralnetworks, the most typical of which is sigmoid function used as anactivating one, which is defined as follow.

FðzÞ ¼ 11þ e�lz

2 ð0;1Þ for l > 0 ð8Þ

The basic structure of a typical feed forward neural network isshown in Fig. 1.

Multilayer perceptron, used in the present study, can be re-garded as the most widely accepted structure of neural networks,applied to model physical phenomena [25]. Regarding the intercon-nections within the ANNs structure, feed forward networks, wereselected. Training the ANNs is adjusting the weight of the neuronsso that the networks output will converge to the desired output. Inthis study, training artificial neural networks was performed basedon the supervised learning method using error back propagationlearning algorithm. The network receives a set of inputs and corre-sponding output examples, applies these examples to learn anddetermine the relationship between the inputs and the output.The networks process the inputs and compare theirs resulting out-put against the expected output. Then, errors are propagated back-

ward through the structure, leading the networks to graduallymodify the weights until the desired output is created [25].

In this study, 82% of the data set was used randomly to train thenetworks and 18% to test the networks. Training the networks con-tinues until the number of certain epoch (100) is passed or the er-ror variations of network prediction in a mentioned epoch areminimized. In addition, principal component analysis (PCA) wasused to process the input features into new component withoutcorrelation in order to achieve the simple interpretation of infor-mation. Also, each variable was normalized by subtracting fromits mean value and then dividing the result by its standard devia-tion so as to have zero mean value and unity variance for all vari-ables. In this study, neural networks have at least one hidden layerwith the number of neurons being approximately between half andtwice the number of input features (4 and 17) and at most two hid-den layers with five fixed neurons in the first layer and the numberof neurons in the second layer being between 4 and 17. The net-works were developed in MATLAB software 7.12 (R2011a) [43].

The learning algorithm of the networks determines the initialweights randomly and then calculates the set of connected weightsto the least error through error optimization method of gradientdecent [43]. The random initial value of connected weights is ofgreat importance in networks performance. Therefore, randomsearch and evolution methods like genetic algorithm are used todetermine the optimal values for the initial weights of neural net-works [44]. In this study, weight values of the networks composeof real chromosome genes such that each gene could be a realnumber. The evolution process of this chromosome took place in100 generations under standard combination and mutation. Final-ly, the best response in 100 generations was recorded. To do this,genetic algorithm program along with MATLAB software was used.

2.4. Polynomial regression technique construction

Polynomial regression model is one of the most commonly usedtechniques in statistics. It can cover a variety of mathematicalmethods such as linear and nonlinear relationships. In this study,two types of polynomial multiple models i.e. multiple linear andquadratic regression were used. Multiple linear regression (MLR)is one of the simple polynomial models used for linearly modelingthe relationship between more than two variables. This model canbe simply formulated as follow [26].

Y ¼ b0 þ b1x1 þ b2x2 þ � � � þ bpxp þ e ð9Þ

where Y is the response variable, x is the predictor variables, b is thelinear effect coefficients and e is the random error.

Multiple quadratic regressions (MQRs) are also one of the usefultypes of polynomial models used for modeling the relationshipamong more than two variables nonlinearly. This model can be for-mulated as follow [26].

Fig. 1. Basic structure of a typical feed forward neural network.

368 M. Aliabadi et al. / Applied Acoustics 74 (2013) 364–374

Y ¼ b0 þ b1x1 þ b2x2 þ � � � þ bpxp þ c1x21 þ c2x2

2 þ � � � þ cpx2p þ e

ð10Þ

where Y is the response variable, x is the predictor variables, bp isthe linear effect coefficients, cp is the quadratic effect coefficientsand e is the random error.

2.5. Evaluation criteria

Root mean square error (RMSE) and coefficient of determination(R2) are commonly used in order to assess the performance of pre-diction techniques. These criteria determine the differences be-tween predicted values and actual values of the subject [26]. R2

is used to show the similarity between the model tendency andthe measured data, with higher R2 values representing greater sim-ilarities. Furthermore, RMSE indicates the estimation accuracy.Lower RMSE values represent more accurate estimations. TheRMSE and R2 criteria can be formulated as follows.

RMSE ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPnj¼1ðTj � YjÞ2

n

sð11Þ

R2 ¼ 1�PðTj � YjÞ2P

T2j �

ðP

TjÞ2

n

ð12Þ

where Tj and Yj are the measured and the predicted values of data j,respectively; and n represents the number of measurements.

3. Results

3.1. Performance of noise prediction techniques

To attain the empirical noise prediction technique in industrialembroidery workrooms, ANNs with the different structures weredeveloped. The results showed that prediction errors of all differ-ent structures of ANNs were in the acceptable level between 0.69and 0.98 dB, based on performance in predicting test data. Further,it was found that network structure with one hidden layer and 14neurons exhibited the highest level of accuracy. As expected, pre-diction errors in the training phase were lower than the test phase.Comparison of different structures of ANNs in the train and the testphases was shown in Table 3. The scatter plots of measured valuesof noise level compared with prediction values by the best neuralnetworks in the train and the test phases were presented in Figs.2 and 3. Variations of prediction error of the networks with onehidden layer in terms of the number of neurons in the train andthe test phase were presented in Fig. 4.

The results showed that an increase in the number of neurons inthe hidden layer resulted in a decrease in the prediction error inthe train and the test phases. To attain the simpler noise predictiontechniques to derive ready to use empirical formulations, multipleregression with different structures were employed. The compari-son of multiple regression performance was done based on RMSEand R2 as shown in Table 4.

The results showed that compared with multiple regressiontechniques, ANNs were more accurate in noise prediction. A com-parison of the RMSE in Tables 3 and 4 showed that the predictionerrors of the two approaches were approximately within theacceptable level. But, based on coefficient of determination values,regression techniques did not exhibit reasonable predictionaccuracy.

However, quadratic regression technique (with RMSE = 1.2 dB)performed slightly better than did linear regression technique.Multiple linear regression equation for predicting noise level wasformulated as follow.

Y ¼ 85:311� 0:409ðMTÞ � 0:121ðNEÞ þ 0:725ðNHÞþ 0:333ðELÞ þ 0:380ðFTÞ þ 0:785ðESÞ � 0:007ðRTÞþ 0:154ð�aÞ � 0:552ðRÞ ð13Þ

Multiple quadratic regression equations for predicting noise le-vel was formulated as follow.

Y ¼ 85:290� 0:326ðMTÞ � 0:396ðNEÞ þ 1:091ðNHÞþ 0:306ðELÞ þ 0:356ðFTÞ þ 0:687ðESÞ � 0:541ðRTÞ

� 1:007ð�aÞ � 0:286ðRÞ þ 0:512ðNEÞ2 � 0:624ðNHÞ2

� 0:055ðELÞ2 � 0:013ðFTÞ2 � 0:015ðESÞ2 � 0:040ðRTÞ2

þ 0:399ð�aÞ2 � 0:151ðRÞ2 ð14Þ

Note that, all the dependent features defined in these equationsare normalized. The scatter plots in Figs. 5 and 6 illustrate the mea-sured values of noise level as well as the values predicted by themultiple regression techniques for unseen data set.

The observations, that are larger or smaller than the other val-ues in the data set of a feature, are called outlier observation. Infact, an outlier observation is the one which is located in a distancefarther than the other data. The outlier observations were deter-mined based on the statistical criteria of variables mean and vari-ance with the confidence interval of%95. If any data were locatedout of the range mean ± 2SD, it would be taken as outlier observa-tion. Therefore, in order to develop a more accurate technique, 17observations were separated. Then, new ANNs with different struc-tures were developed. Performance of the new structures of ANNsin the train and the test phases were shown in Table 5.

Table 3Comparison of different structures of neural networks techniques in the train and the test phases.

Modeling phase Train Test

Evaluation criteria RMSE (dB) R2 RMSE (dB) R2

ANNs (one hidden layer with different neurons) 0.64–0.94 0.76–0.90 0.69–0.98 0.79–0.90Best ANNs (one hidden layer with 14 neurons) 0.67 0.90 0.69 0.88

Fig. 2. Comparison between measured and predicted LAeq values using the best ANNs for the train data.

Fig. 3. Comparison between measured and predicted LAeq values using the best ANNs for the test data.

M. Aliabadi et al. / Applied Acoustics 74 (2013) 364–374 369

The results showed that, the performance of the new ANNs wasmore suitable than that of former ones. However, the differences inprediction error were not considerable. Variations of prediction er-ror of the networks with one hidden layer in terms of the numberof neurons in the train and the test phase were presented in Fig. 7.

The results showed that the prediction error of neural networkswith two hidden layers did not change remarkably with the in-crease of the number of neurons. In addition, two hidden layerednetworks had greater prediction error than one-layered ones.

The scatter plots of measured values of noise pressure level incomparison with values predicted by the best neural networks(with one hidden layer and 17 neurons) in the train and the testphases were presented in Figs. 8 and 9.

3.2. Sensitivity analysis

Neural networks have been censured for being black boxsolutions, i.e. because of their incapability in forming interpret-

able parameters for each input variable. To reduce this problem,sensitivity analysis may be performed in order to simplify theirinference mechanism. Sensitivity analysis, an approach to theanalysis of relationships between the inputs and outputs of themodel, investigates the model response and evaluates the accu-racy of the model. It is executed by building systematic variationin input features of models and monitoring the effects of thisvariation in output variable. During sensitivity analysis, thelearning process is disabled; therefore, network weights are notaffected. In order to analyze acoustical and process features thatinfluence the noise level of industrial embroidery, sensitivityanalysis of the best neural network without PCA was applied.In our study, every input variable in the network was varied be-tween the mean ± 3SD, while all others fixed at their respectivemeans, and the corresponding change in the output was re-corded as a percentage deviation. The results of the sensitivityanalysis of the input features of the best ANNs were presentedin Fig. 10.

Fig. 4. Prediction errors of neural networks with one hidden layer in terms of the number of neurons.

Table 4Comparison of the different multiple regression techniques in the train and the testphases.

Modeling phase Train Test

Evaluation criteria RMSE (dB) R2 RMSE (dB) R2

Multiple linear regression 0.99 0.74 1.24 0.61Multiple quadratic regression 0.9 0.79 1.2 0.65

Fig. 5. Comparison between the measured and the predicted Leq using linear regression for the test data.

370 M. Aliabadi et al. / Applied Acoustics 74 (2013) 364–374

Among the features of embroidery process, two featuresnamely, the embroidery lifetime and the operation speed are foundto be the most important factors influencing the noise level. Inaddition, the average of the absorption coefficient was the mostimportant acoustic feature that influenced the noise level of eachworkroom. On the other hand, the maker model type, the numberof embroideries and heads and the fabric type are found to be lessaffective parameters. However, the implementation of only themost effective features for developing prediction model decreasesthe average prediction accuracy; because residual features have acomparable power additively. Deviation of the noise level (dB) inembroidery workrooms caused by change in any input features be-tween the mean ± 3SD was presented in Fig. 11.

3.3. Case study

One of the most important applications of the developed tech-nique was in determining the effectiveness of the different inter-

ventions based upon the input features of neural networks forreducing the noise level lower than the noise exposure limits. In or-der to test this application in practice, one of the noisy embroideryworkrooms was selected. Using the best neural networks, the noiselevel was predicted based on the different features of the work-room. In addition, the noise levels for different situations in theworkroom were predicted. Different situations were defined by fivescenarios. Four scenarios were changing the operation speed, usinga newer embroidery machine, substituting the used fabric type andchanging the acoustic condition of workroom from live to averageroom, respectively. In the fifth scenario, the mentioned four scenar-ios were implemented simultaneity. The results of the prediction ofnoise levels in the different scenarios were shown in Table 6.

The results showed that the developed empirical technique cansuitably predict the effectiveness of the different changes on thenoise level of embroidery workrooms. Acoustics analysis of thetypical embroidery workrooms based on the developed techniquecan facilitate using the graphical user interface (GUI). It was notedthat, design of graphical user interface based on the best neuralnetworks which is able to predict the noise of embroidery work-rooms as a simple practical tool was performed.

4. Discussion

Predicting noise level in noisy environments is considered to bean important aspect of engineering noise control process. Neural

Fig. 6. Comparison between the measured and the predicted Leq using quadratic regression for the test data.

Table 5Comparison of the new different structures of ANNs in the train and the test phases.

Modeling phase Train Test

Evaluation criteria RMSE (dB) R2 RMSE (dB) R2

ANNs (one hidden layer with different neurons) 0.48–0.88 0.70–0.90 0.57–0.93 0.51–0.90ANNs (two hidden layers with different neurons) 0.77–0.94 0.69–0.79 0.81–0.99 0.43–0.81Best ANNs (one hidden layer with 17 neurons) 0.48 0.90 0.57 0.90

Fig. 7. Prediction errors of the new neural networks with one hidden layer in terms of the number of neurons.

Fig. 8. Comparison between measured and predicted LAeq values using the best ANNs for the training data.

M. Aliabadi et al. / Applied Acoustics 74 (2013) 364–374 371

Fig. 9. Comparison between measured and predicted LAeq values using the best ANNs for the test data.

Fig. 10. Sensitivity analysis of the input features of the best ANNs.

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networks have been used as an alternative approach to predict thecomplex phenomena such as acoustics [29]. The analysis of the re-sults based on the performance criteria indicated that feed for-ward, multilayer neural networks could accurately predict thenoise exposure level in industrial embroidery workrooms in termsof acoustical parameters of workrooms and technical characteris-tics of real noise sources. The prediction error of the developedANNs techniques was lower than the subjective difference thresh-old for sound pressure level ± 1 dB mentioned in ISO 3382 [45].Therefore, the ANNs, if used properly, seem to be an ideal tech-nique for the purpose of predicting the noise level and in achievingthe precision grade of accuracy [42]. One the most important appli-cations of prediction models with this level of accuracy can be de-fined for determining the effectiveness of various solutions of anoise control measures in different phases of establishment andutilization of industrial process.

The error rate of the developed technique was at an acceptablelevel compared with similar studies, which used neural networksto predict sound level in architectural and environmental acousticsareas [30–32]. In addition, ANNs exhibited higher performance inpredicting noise level, compared with MLR and MQR. This demon-strates that the soft computing system can be a good tool for min-imizing the uncertainties in the noise prediction approaches.

Multiple regression analysis cannot provide accurate predic-tions of the noise level, because this approach is restricted to hu-man capabilities. The ANNs simplifies the modeling process byremoving the need for minimum essential theories. However, for

user, regression techniques, as empirical methods, seem to bemore practical than artificial neural networks. The performanceof the regression techniques showed that the developed multipleregression method may be used for predicting the noise level inachieving the survey grade of accuracy [42]. Determining highlynoisy workrooms compared with other similar workrooms canbe facilitated using prediction models within the survey grade ofaccuracy.

To attain the networks with optimal performance, structural,activation function and learning algorithms were chosen in accor-dance with the nature of the phenomenon of study [25]. In addi-tion, the best networks were chosen using trial and error interms of the number of hidden layers and their neurons. The pre-diction error of networks with one hidden layer showed a descend-ing trend with the increase of the number of neurons which can bedue to the use of more nonlinear features and better learning of thenetworks [46]. On the other hand, if the networks have too manyhidden layers and neurons, they will follow the noise in the obser-vations due to over fitting, resulting in too weak generalization foruntrained data. One practical way to achieve the expected modelaccuracy is to begin with a small number of hidden layers and in-crease on as needed. Using the genetic algorithm to determine theoptimal structural of the networks has been proposed as a suitableapproach. In this study, genetic algorithm could improve the per-formance of the networks through determining the optimal valuesof the initial weights. The results confirmed the high capabilities ofartificial intelligence approach to improve the performance of

Fig. 11. Deviation of noise level caused by change in any input features between the mean ± 3SD.

Table 6Predicted noise levels based on the different scenarios in the investigated case.

Model features MT NE NH EL FT ES RT �a R Measured Leq (dB) Predicted Leq (dB)

Case situations 1 1 18 14 6 850 2.8 0.05 8.98 88.5 89Option 1 1 1 18 14 6 750 2.8 0.05 8.98 – 86.5Option 2 1 1 18 2 6 850 2.8 0.05 8.98 – 87.2Option 3 1 1 18 14 2 850 2.8 0.05 8.98 – 86.5Option 4 1 1 18 14 6 850 0.95 0.14 29.6 – 87.1Option 5 1 1 18 2 2 750 0.95 0.14 29.6 – 83.9

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noise prediction technique compared with current empirical tech-niques [32,33].

The main sources of prediction error in the noise predictiontechniques may be the type and the number of selected featuresfor modeling. In real situations, there are many other parametersthat can influence the noise level; however, all of them cannot beincorporated in the model. Note that, the level of detail of inputfeatures should be in accordance with the desired value of theaccuracy of results [42].

In addition, in this study, simplicity and free complex calcula-tions were considered to select the input features of noise predic-tion technique. Since neural networks are the data-orientedmethods, selecting the study field, the type of features and thenumber of observations and preparing them correctly are of greatimportance for the purpose of developing the networks with opti-mal performance. Therefore, the main reasons for selecting indus-trial embroidery process as the study field were similarity ofembroidery process and shape of structure in all of the studiedembroidery workrooms. Although our technique achieved anacceptable level of validity in predicting the noise, the applicationdomain of this technique was restricted to this field type. The re-sults of the sensitivity analysis showed the most important fea-tures are embroidery lifetime, operation speed and average of the

absorption coefficient. Due to the lack of appropriate preventivemaintenance system, the embroidery machines which were olderproduced the highest level of noise. For all textile machinery, somereductions in noise level can be achieved by preventive mainte-nance. Maintenance of machines which includes such action aslubricating, replacing the old spare parts with new ones and con-trolling the vibration of moving parts is crucial for embroideryequipment in order to operate quietly.

Operation speed of embroidery heads (in terms of stitches perminute) can also influence the noise level of machines. The highspeed of needles in striking on the work surface can also increasethe noise level in workrooms. The type of the fabric used underthe needles for embroidery operations also affects the noise level.In real situations, the fabrics with more strength and thicknesscan reduce the contact area of the needles to work surface so thatthe quiet operations will be assured.

In addition, subjective acoustic characters of embroidery work-rooms were in the range of live to medium live due to using typicalhard constructional materials (35). Therefore, using materials withsuitable sound absorption coefficient will improve the acousticperformance of workrooms resulting in reduced noise level. Obser-vations to be used for training ANNs should be large enough to cov-er the possible known variations in the subject domain. From a

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practical point view, the domain of this prediction technique couldbe increased on through changing constructional materials in someworkrooms and recording the new observation values for trainingand developing neural networks. But there were limitations forperforming these treatments considering the required budgetand the limitations of the production process.

As the noise level of embroidery workrooms was approximatelywithin the range of 80–90 dB, the designer can change the struc-tural, acoustical or procedural characteristics of workroom in thefeasibility phase, employing a procedure similar to the one usedin the case study and presented in the form of the graphical userinterface (GUI) and if necessary, may establish a new workroomso that attaining the optimal conditions compared with the occu-pational noise exposure limit of 85 dB is facilitated. It is noted that,the code of the program written in this study is available upon therequest through the author’s email.

5. Conclusions

The empirical technique of noise prediction presented in thisstudy was of suitable validity in specific user domain in industrialembroidery workrooms compared with other techniques. In addi-tion, this technique could predict the noise level based on real sit-uation of typical industrial process. It is obvious that there wereother acoustical and processing features, which in addition to theselected features could influence the workrooms noise level. In thisregards, complementary research could be performed to achievefurther improvements. From a practical point of view, this tech-nique is a helpful tool for the analysis of noise level in differentphases of design, establishment and utilization of embroideryworkrooms. Using artificial intelligence approaches in order to de-velop valid technique of noise prediction based upon real industrialprocess situations and acoustical characteristics of the workroomscan improve the efficacy of hearing conservation programs andmay provide valuable information for designer, architects andacoustics professionals.

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