Research Article
Formulation of noise isolation index for evaluating the interior acoustics level in vehicle cabin
Advances in Mechanical Engineering 2018, Vol. 10(4) 1–15 Ó The Author(s) 2018 DOI: 10.1177/1687814017708153 journals.sagepub.com/home/ade
Adel Mohammed AlDhahebi1, Ahmad Kadri Junoh1, Afifi MD Desa1, Mohd Hafiz Zakaria1 and Amran Ahmed1
Abstract This article proposes noise isolation index to assess the acoustical comfort level in the interior vehicle cabin. The noise isolation index is developed, validated, and compared for three local compact cars, namely, Axia, Myvi, and Viva which are most widely used vehicles in Malaysia and have not been yet studied. Toward this end, the interior and exterior sound signals are recorded on both stationary and non-stationary (highway, pavement, and urban) conditions and the trends of sound quality loudness (sone) and sharpness (acum) with respect to sound pressure level (Pa) are observed. The findings of this study indicate that the noise isolation index results are literally influenced by factors including type of road surface, engine transmissions, and vehicle design characteristics. The proposed index can be successfully extended by automotive researchers to assess the interior vehicle noise and can be significant factor in vehicle manufacturing process. Keywords Noise isolation index, acoustics, sound quality metrics, riding comfort, sound pressure level, experimental design
Date received: 21 November 2016; accepted: 16 March 2017 Handling Editor: Truong Quang Dinh
Introduction Acoustical comfort has recently become an essential factor that is considered by customers when purchasing a new car.1,2 Improving comfort level in vehicles plays a significant role in attracting customers to purchase a vehicle that meets their expectations and demands. Nowadays, with advancements in technology and improved socioeconomic conditions, the expectations of customers for a lighter and comfortable vehicle have increased, leading to stiffer competition among automotive manufacturers to improve the level of comfort in the interior vehicle cabin. Consequently, as automotive products become more competitive, substantial efforts and vigorous investments are constantly undertaken to develop methods to enable car manufacturers to adequate their products to the customers’ demands.3,4 The acoustical comfort in the vehicle
interior cabin is obviously influenced by a variety of factors such as airborne noise, structure-borne noise, engine, tire–road interactions, and interior space vibrating parts.5–7 In this regard, over the past decades, multiple studies have been done purposely to investigate and evaluate the level of comfort inside the car cabin and to estimate the effects of exposed noise vibration on the driver health and concentration.7–11 Studying the acoustical comfort in vehicles is essential task in front of acoustics engineers when designing
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Institute of Engineering Mathematics, Universiti Malaysia Perlis, Arau, Malaysia
Corresponding author: Adel Mohammed AlDhahebi, Institute of Engineering Mathematics, Universiti Malaysia Perlis, Pauh Putra, 02600 Arau, Perlis, Malaysia. Email:
[email protected]
Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/ open-access-at-sage).
2 and manufacturing a vehicle. This is due to the recent ever increasing demands for suppressing and reducing undesirable noise in the vehicle interior cabin. In fact, undesirable sounds experienced in vehicles can cause disturbance and health disorders. The severity of noise vibration on human depends on the frequency, period of exposure, magnitude, and ergonomic factors. The period of exposure to noise is important because longterm exposures to noise could result in hearing loss.12,13 Hearing loss is inevitable especially at higher frequencies such as 4 kHz.14 While at low frequencies such as below 1 Hz, exposure leads to motion sickness, 5–6 Hz troubles the stomach, and around 20 Hz range affects the head and neck.15 To overcome these undesirable sounds, the conventional vehicle designs and material choices must be revisited. As a consequence, various testing procedures with enhanced tools have been constantly developed to enable car manufacturers to adequate their products to the customers’ expectations and demands. Basically, humans evaluate the acoustical comfort based on the perceived sound quality that the automotive product makes. In this case, there are few factors that contribute to the judgment of human assessment and feelings toward the comfort level, which are physiological variables, for instance, feelings of temperature, air speed in the cabin, acceleration, light type intensity, and ergonomics. However, this type of assessment is time-consuming and more importantly produces irreproducible results as it relies on the subjective impressions made by subjects which obviously are difficult to be represented in quantitative scale. Under the conditions that the human perceptions toward experienced comforts are complex and hard to be estimated, various methods and models have been proposed over the years such as Discrete Wavelet Transform (DWT),16 booming index,4 jury subjective assessments,17 artificial neural networks and signal processing, back propagation neural network,18 and machine learning.19 Moreover, the vehicle acoustic comfort index (VACI) was proposed for assessing the annoyance levels in the vehicle interior cabin.6,8,20–23 More recently, an evaluation index called Sound Metric based on the Wigner–Ville distribution (SMWVD) is developed for investigating the sound quality of shock rattling noise in the vehicle.24 The proposed index proved to be useful for the evaluation and minimization of the rattling sound quality in automobiles. Throughout the literature, we found that it is extremely difficult to completely produce a vehicle with no noise, but it is essential to control and minimize sources of noise vibration and converting the annoying sounds into more pleasant in order to improve the acoustical level and therefore give people a better
Advances in Mechanical Engineering driving experience and environment. Thus, the fact remains that there is no unique holistic solution but rather a complex set of results with restricted reliability which means the solutions produced are restricted and that the methods proposed and employed for assessing the noise in the interior cabin should be investigated further. In addition, suppressing undesirable sounds and improving comfort in the interior vehicle cabin remains one of the key challenges in automotive industries. Based on the above considerations, this article proposes a novel formulation of noise isolation index (NII) to evaluate acoustical comfort in vehicles. Unlike the previous studies which focused on only one or two types of the interior noises, this study aims to consider both interior and exterior sounds to evaluate the overall acoustics level in the interior vehicle cabin. The proposed index is based on categorizing the sounds into interior and exterior based on the experimental design and data collection procedure. The NII index is developed and validated for three local compact cars, namely, Axia, Myvi, and Viva which are the most widely used cars in Malaysia according to the Malaysian Automotive Association (MAA). The results of the proposed index are compared using statistical analysis. The rest of this article is organized as follows. In section ‘‘Methods and materials,’’ experimental design was carried out to acquire the sound signals using three compact cars, Axia, Myvi, and Viva, on both idle and running (highway, pavement and urban) conditions. In addition, the measured sounds were analyzed to obtain the sound quality metrics loudness and sharpness and sound pressure levels with respect to the changes in engine speeds, types of cars, and types of road surfaces. In section ‘‘Data analysis,’’ statistical analysis of the observed sound quality parameters with sound pressure levels was conducted to analyze and identify the correlation and significance between the sound quality metrics, sound pressure levels, and the independent variables; types of engine speeds, types of cars, and roads. The development of the NII is presented in section ‘‘NII.’’ Section ‘‘Results and validation’’ elaborates the results and validation of the proposed index. Finally, the summary of this article is presented in section ‘‘Conclusion.’’
Methods and materials This section comprises two parts: the first part concerns about developing experimental design to acquire the sound signals. By conducting the experimental design, the dependent (response) and the independent (explanatory) variables were identified. In this case, the
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dependent variable is the acoustical comfort and the independent variables include types of road surfaces, types of cars, and types of engine speeds. In addition, the period of recording, placements of microphones, selection of the test equipment, identifying the measurement units, and the selection of an experienced driver were carefully carried out to ensure reliable data acquisition while minimizing variations. Meanwhile, the second part is devoted to assess the measured sound signals to observe the changes in sound quality and sound pressure levels with respect to the independent variables. Both parts are essential toward the development of NII because they serve as the initial stages to recording, visualizing, and interpreting sound signals. Figure 1 depicts the flowchart of this study.
Start
Experimental Design LabVIEW Microphones Sound recordings
Highway
Pavement
Stationary
Urban
Sound Quality Software
Sound Quality Sound Pressure
Loudness
Sharpness
Correlation Analysis Multiple Regression Analysis
Noise Isolation Index (NII ) Analysis of Variance (ANOVA)
Conclusion
Figure 1. Experimental design and the methodology flowchart.
Experimental design The experimental design is carried out to identify the effects of the independent variables (types of road surface, types of cars, and types of engine speeds) on the acoustical comfort (response variable). The importance of this section is to ensure accurate recording of sound signals while minimizing errors and variations. The complete procedure of sound signal acquisition is presented in the following subsection.
Data acquisition procedure The sound signals are acquired using NI cDAQ-9174 data acquisition system, microphones model (GRAS General Purpose Array, Microphone: 50 mV/Pa), and LabVIEW NI-DAQmx software. This acquisition system provides high-speed data recording, flexibility, portable, and easy to be implemented with LabVIEW, Visual Basic, NET, C/C++, and so on. Microphones are used for better sound tracking and accurate recording of sounds. In this study, the major attention was to record the interior and the exterior sounds concurrently. Thus, four microphones are used to record the acoustic signals, whereby two microphones are placed on the interior front windows (left and right) and two microphones are placed on the exterior front windows (left and right). Figure 2 shows the experiment recording and the placement of the two microphones in the interior/exterior front window in a symmetrical way. As for the test cars, three local compact cars, namely, Axia, Myvi, and Viva which are shown in Figure 3 were used in the experiments. The specifications and descriptions of the tested cars are presented in Table 1. In addition, the measurements of sounds are carried out at four driving states, namely, idle, highway, pavement, and urban. The description of the measurement conditions and tested road surfaces are described in Table 2 and Figure 4, respectively. In addition, measurements of sound signals were done against the changes in engine speed (r/min). At idle state of driving (stationary), the measurements are not concrete since the car tires are not rolling on the road surface (with zero excitation of tire–road). Meanwhile, if the car is at driving state (highway, pavement, and urban), the measurements depend mainly on the speeds of the engine (r/min) and the type of road surface. The tested engine speeds (r/min) throughout the measurements are illustrated in Table 3. Moreover, the experiments were conducted by two members, a driver and assistant. The role of the driver is to drive the test car while maintaining speeds specifically according to the test plans. The assistant’s role is to handle the notebook computer and record the sounds. To ensure the statistical precision and reasonability of the recorded sounds, the duration of
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Table 1. Tested cars’ specifications. Car/specifications
Axia
Myvi
Viva
Engine type Height/width/length (overall) (mm) Height/width/length (interior) (mm) Min. turning radius (tire) (m) Max. torque (DIN) N m (kg m)/r/min Engine transmission
1KR-DE2 1510/1385/1900 1240/1385/1900 4.5 90/3600 1.0 Standard. Auto
K3-VE 1570/1665/1850 1265/1380/1265 4.7 117/4400 1.3 Standard. Auto
ED-VE 1230/1475/1675 1250/1300/1845 4.2 90/3600 1.0 Standard. Auto
DIN: Deutsches Institut fu¨r Normung (German industry standard).
Table 2. Measurement conditions, descriptions, and locations of the tested roads. Conditions
Description
Location
Idle (stationary)
The vehicle is in parked without any tire excitation with road Moderate effects with road Strong effects with road surface Less effects with road surface
IMK Pauh Putra, Arau
Driving state (highway) Driving state (pavement) Driving state (urban)
Along (Pauh-Arau) highway Pauh Putra (in front DK1), UniMAP campus Kampong road, Arau
Figure 2. Acoustic signal recording of Myvi on stationary condition: (a) microphones placement on the right window front and back (interior and exterior) and (b) recording using LabVIEW and a laptop.
measurements is set to 10 s. The experiments were repeated seven times in order to ensure the reliability and accuracy of the data because encountered errors might occur during the tests. Apart from this, it is worth considering the acknowledge that the sound measurements in the vehicle interior cabin imply some challenges such as effects of external environment, speed changing, and microphone placements. To overcome these challenges, four criteria are followed including the appropriate placement of the microphones, sealed windows and doors, air conditioning switched off, and manual control of the wheel and speed by experienced driver according to the test plan. Besides the accurate attachment of microphones, the
movements of the driver and the assistant are absolutely restricted during the recording periods. Furthermore, all measurements were conducted at night to eliminate the effects of pass-by vehicles. And aerodynamic noises caused by wind and fluctuation pressure are dominant at higher engine speeds that are beyond the scope of this article.
Evaluation of acoustic levels Generally, the noise experienced in the car can be assessed by means of objective measurements (response, frequency, etc.) and by subjective assessment test. The latter is particularly long and involves complex setup as
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Figure 3. The test cars: (a) Axia, (b) Myvi, and (c) Viva.
Figure 4. Tested road surfaces: (a) highway, (b) pavement, and (c) urban road surfaces (locations: Arau, Perlis, Malaysia).
Table 3. Engine speeds (r/min) employed in the test. Condition
Engine’s r/min
Stationary (Idle)
1000, 1250, 1500, 1750, 2000, 2250, 2500, 2750, 3000 1000, 1250, 1500, 1750, 2000, 2250, 2500, 2750, 3000 1000, 1250, 1500, 1750, 2000, 2250 1000, 1250, 1500, 1750, 2000, 2250
Highway Pavement Urban
it requires the subjects to sit in each test car and listen to a pre-defined sound sample played through the sound system. In this study, the noise experienced in the car’s interior cabin is assessed through the objective test which involves sound measurements and analysis. Figure 1 shows the experimental design and the methodology flowchart that describes the flow and the research study. After completing the recordings of the acoustic signals, the recorded sounds are analyzed using
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Table 4. Sound quality parameters description. Sound quality parameter
Description
Loudness Sharpness
The main metrics that is designed to characterize the awareness of human to the noise strength Represents the high frequency of the noise signal
Figure 5. The sound quality parameters for highway condition, recording at 1000 r/min.
Sound Quality Metrics Software (Bru¨el & Kjær). The sound quality can be interpreted as a measure of the accuracy of the measured sound waves, and it also refers to the level of comfort and physical difficulties that are experienced by the listener toward the perceived sound. The concept of sound quality was defined by Blauert25 as the anthropic appropriateness of sounds under a specific technical target or task. Two trends of sound quality named Zwicker loudness (sone) and sharpness (acum) were selected and their changes observed against the engine speeds (r/min). The sound quality parameters, loudness and sharpness, used in this study are described in Table 4. The reason we selected the sound quality (loudness and sharpness) is their sufficient and effective description of the measured sounds. The sound quality metrics loudness and sharpness of
the measured sounds were analyzed for both stationary and non-stationary (highway, pavement, and urban) conditions with respect to the tested engine speeds for the three test cars Axia, Myvi, and Viva in all the seven recordings. The sound quality loudness and sharpness are categorized into exterior (right and left) metrics and interior (right and left) metrics according to the measured sounds. Figure 5 is an example illustrating the sound quality metrics analysis of Myvi on highway road surface, recording at engine speed of 1000 r/min.
Data analysis Data analysis is carried out purposely to determine the level of significance between the observed loudness and sharpness and engine speeds. It is also carried out to
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Figure 6. Scatter plot with regression for the exterior noise of Axia on highway road surface.
Figure 7. Scatter plot with regression for the interior noise of Axia on highway road surface.
ensure the observed sound quality is accurate and valid for further processing. Toward this end, the multiple linear regression model is developed to identify the correlation and the relationship between the observed sound quality with respect the engine speeds. The purposes of conducting multiple regression analysis are to predict, explain, and or to formulate a theoretical concept on the link between two or more independent variables and a dependent variable. In this case, the independent variables are the loudness and sharpness, while the dependent variable is the sound pressure. In this regard, in order to determine the level of significance for the loudness and sharpness that corresponds exactly with the changes in the speed of the engine, we must consider satisfying two criteria. The first criterion is the R2 value which describes the indicator of the regression between parameters of sound quality and the engine speed (r/min). The value of R2 must be above 50%. In addition, the R2 represents the correlation level between the engine speed (r/min) and
loudness and sharpness. The second criterion is the pvalue which describes the correlation level between the sound quality parameters and engine speed (r/min). The value of p must be smaller than 0.05 to achieve the level of significance. The data analysis results of the tested cars are presented in the following subsections.
Axia Figures 6 and 7 show the scatter plots with regressions of the exterior and interior acoustics for Axia on highway road surface, respectively. Table 5 shows the results of multiple linear regressions of Axia on highway road surface; Figures 8 and 9 present the results of interval plots for the exterior and interior sound quality of Axia on highway road surface.
Myvi The results of the multiple linear regression model of Myvi on highway road surface are displayed in Table 6
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Table 5. Multiple regression analysis results of Axia on highway road surface.
Loudness (sone) Sharpness (acum)
T-value p-value Regression value (R2) T-value p-value Regression value (R2)
Exterior
Interior
53.61 0.000 98.4% 29.82 0.000 98.4
2.71 0.008 79.4 5.11 0.000 79.8
Table 6. Multiple regression analysis results of Myvi car.
Loudness (sone) Sharpness (acum)
T-value p-value Regression value (R2) T-value p-value Regression value (R2)
Exterior
Interior
81.47 0.000 99.2 29.53 0.000 99.3
4.14 0.000 61.2 0.20 0.840 60.1
below. Meanwhile, Figures 10 and 11 present the results of interval plots for the exterior and interior sound quality of Myvi on highway road surface, respectively.
Viva The results of the multiple linear regression model of Viva on highway road surface are described in Table 7 below. And Figures 12 and 13 present the results of interval plots for the exterior and interior sound quality of Viva on highway road surface, respectively.
NII NII is formulated to evaluate the acoustical comfort inside vehicle cabin by considering the observed sound quality of both interior and exterior recorded sounds. The NII index aims to establish mathematical expression which identifies the impact of both interior and exterior noises on the overall comfort level in vehicle cabin. Thus, to formulate the NII, the exterior and interior sounds are first recorded and analyzed as illustrated in the earlier section to obtain the sound quality loudness and sharpness and the relationship between these parameters with engine speeds through the mathematical modeling. The principle employed for this case is the multiple linear regression analysis. The mathematical model developed to represent the NII is expressed in equation (1) and (2). The NII is the
Figure 8. The changes in sound quality’s parameters over engine revolutions (r/min) for the exterior sound quality of Axia on the highway road: (a) the right side of Axia and (b) the left side of Axia.
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Figure 9. The changes in sound quality’s parameters over engine revolutions (r/min) for the interior sound quality of Axia on the highway road: (a) the right side of Axia and (b) the left side of Axia.
Figure 10. The changes in sound quality’s parameters over engine revolutions (r/min) for the exterior sound quality of Myvi on the highway road: (a) the right side of Myvi and (b) the left side of Myvi.
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Figure 11. The changes in sound quality’s parameters over engine revolutions (r/min) for the interior sound quality of Myvi on the highway road: (a) the right side of Myvi and (b) the left side of the Myvi.
Figure 12. The changes in sound quality’s parameters over engine revolutions (r/min) for the exterior sound quality of Viva on the highway road: (a) the right side of the Viva and (b) the left side of Viva.
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Figure 13. The changes in sound quality’s parameters over engine revolutions (r/min) for the interior sound quality of Viva on the highway road: (a) the right side of the Viva and (b) the left side of the Viva.
Table 7. Multiple regression analysis results of Viva car.
Loudness (sone) Sharpness (acum)
T-value p-value Regression value (R2) T-value p-value Regression value (R2)
Outside
Inside
4.34 0.000 21.6 20.62 0.534 20.3
0.65 0.515 56.2 3.90 0.000 55.5
interior noise (right and left sides) divided by the exterior noise (right and left). The interior noise is the sum of the left and right interior regression values of the sound quality parameters (loudness and sharpness) and sound pressure with respect to engine speeds. The exterior noise is the sum of the left and right exterior regression values of the sound quality parameters (loudness and sharpness) with respect to engine speeds. Then, the mean of the results of both interior and exterior noises is used to calculate the overall acoustical comfort which is represented by NII values Noise isolation index(NII) = NII =
NInterior NExterior
Interior noise Exterior noise
ð1Þ ð2Þ
where NII is the noise isolation index, NInterior is the average value of the sum of the left and right interior noises inside car’s cabin, and NExterior is the average value of the sum of the multiple regression model of the exterior left and right noises outside car’s cabin.
Results and validation The results of the proposed isolation index for Axia, Myvi, and Viva on both stationary and non-stationary (highway, pavement, and urban) conditions are displayed in Table 8 below. By referring to the NII range in Table 9, it can be concluded for the idle condition, the results of isolation indexes for Axia, Myvi, and Viva are noisy with values of 0.90456, 1.259, and 2.8628, respectively. This is due to the fact explained earlier which is when engine speeds increase, while the car is at idle condition, the interior noise increases. However, the results of the NII on running condition (highway, pavement, and urban) vary significantly. For example, on highway road surfaces, the NII results of Axia, Myvi, and Viva are 0.09134, 0.1099, and 0.2187, respectively. This indicates that the Axia produces optimal value of NII on highway road surface compared to Myvi and Viva. Meanwhile, on pavement road surfaces, the NII results of Axia, Myvi, and Viva are 0.263, 0.3799, and 0.6057, respectively. And on the urban road surface, the NII results of Axia, Myvi, and Viva are
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Figure 14. Noise isolation index comparison between the interior right sides of Axia, Myvi, and Viva on the highway road surface.
0.149, 0.527, and 0.348, respectively. Finally, the results of Axia, Myvi, and Viva on highway road surface are 0.091, 0.109, and 0.219, respectively. The results on highway road surface for all the three test cars are within the comfort range. This is because the highway road surface is smoother than the pavement and urban road surfaces. Thus, it can be observed from the results of NII that the Axia has betterment isolation criteria toward the exterior noise followed by Myvi and Viva
under the same conditions. Overall, the type of car as well as the type of road surface and increase in engine speeds impact the comfort level in the interior vehicle cabin. For further validation, the results of the interior (right and left sides) NII for the three cars on highway road surface are compared using the analysis of variance (ANOVA) as show in Figures 14 and 15. The results of the ANOVA validation and comparison tests show that the Axia has the most acoustical comfort
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Figure 15. Noise isolation index comparison between the interior left sides of Axia, Myvi, and Viva on the highway road surface.
Table 8. Noise isolation results of Axia, Myvi, and Viva on stationary and non-stationary conditions. Car condition/NII
Axia
Myvi
Viva
Stationary Pavement Urban Highway
0.90456 0.2625 0.14869 0.09134
1.259 0.3799 0.52744 0.1099
2.8628 0.60569 0.34842 0.2187
NII: noise isolation index.
level when compared to Myvi and Viva. The results indicate that the Axia has the best isolation design when
Table 9. The noise isolation index comfort range. Range
Condition
0:05
