| Article Access Statistics|
| Viewed||12048 |
| Printed||270 |
| Emailed||5 |
| PDF Downloaded||23 |
| Comments ||[Add] |
| Cited by others ||4 |
|Year : 2014
: 16 | Issue : 73 | Page
|Noise in restaurants: Levels and mathematical model
Wai Ming To1, Andy Chung2
1 Macao Polytechnic Institute, School of Business, Rua de Luis Gonzaga Gomes, Macao SAR, People's Republic of China
2 EDMS (Hong Kong) Ltd., Department of Environmental Science, Central, Hong Kong SAR, People's Republic of China
Click here for correspondence address
|Date of Web Publication||11-Nov-2014|
Noise affects the dining atmosphere and is an occupational hazard to restaurant service employees worldwide. This paper examines the levels of noise in dining areas during peak hours in different types of restaurants in Hong Kong SAR, China. A mathematical model that describes the noise level in a restaurant is presented. The 1-h equivalent continuous noise level (Leq,1-h) was measured using a Type-1 precision integral sound level meter while the occupancy density, the floor area of the dining area, and the ceiling height of each of the surveyed restaurants were recorded. It was found that the measured noise levels using Leq,1-h ranged from 67.6 to 79.3 dBA in Chinese restaurants, from 69.1 to 79.1 dBA in fast food restaurants, and from 66.7 to 82.6 dBA in Western restaurants. Results of the analysis of variance show that there were no significant differences between means of the measured noise levels among different types of restaurants. A stepwise multiple regression analysis was employed to determine the relationships between geometrical and operational parameters and the measured noise levels. Results of the regression analysis show that the measured noise levels depended on the levels of occupancy density only. By reconciling the measured noise levels and the mathematical model, it was found that people in restaurants increased their voice levels when the occupancy density increased. Nevertheless, the maximum measured hourly noise level indicated that the noise exposure experienced by restaurant service employees was below the regulated daily noise exposure value level of 85 dBA.
Keywords: Indoor noise levels, occupational exposure, restaurants
|How to cite this article:|
To WM, Chung A. Noise in restaurants: Levels and mathematical model. Noise Health 2014;16:368-73
| Introduction|| |
The restaurant industry employs a large number of people worldwide. The US National Restaurant Association reported that the restaurant industry grows rapidly and employs around 13 million Americans in a million locations, about 10% of the US workforce.  It also estimated that the restaurant trade is expected to reach US$ 660.5 billion sales in 2013. In the UK, the number of caterers including restaurants, cafes, and canteens was 0.43 million, employed 1.6 million people, and contributed GBP 25.2 billion to national gross valued added in 2011.  In Hong Kong, the restaurant industry does not only serve 7.1 million local customers. The restaurant industry also plays an important role to support the tourism industry because Hong Kong attracts >42 million visitors each year. According to the statistics provided by the Hong Kong Census and Statistics Department,  sales and other receipts in the food services trade amounted to HK$ 101.4 million in 2010, increasing by 6.9% from 2009. In that year, there were 13,910 food services establishments. About 5080 of them were Chinese restaurants, 1143 were fast food restaurants, and 7687 were other food services establishments including a wide variety of western and exotic food restaurants. In total, these food services establishments employed 238,276 people; that is, 7.4% of the total number of employees in Hong Kong.
A significant portion of restaurant employees needs to work for over 8 h a day. They are exposed to a wide range of occupational hazards such as cuts, burns, sprains and strains, slips and falls, ,, second-hand smoke, ,, and air pollutants including cleaning chemicals and emissions from cooking fume. ,,,, In addition, service employees are exposed to high noise levels during peak hours in the morning, during the lunch time, and in the evening hours that may affect psychological and physiological well-being. Unfortunately, the extant literature of noise primarily focused on industrial and environmental noise. ,,, While in the indoor environment such as restaurants, researchers primarily focused on indoor air quality, ventilation, thermal comfort, and lighting. ,,,,, In this paper, a mathematical model that describes the noise in restaurants is presented. Noise measurements, geometrical, and operational parameters were recorded in different types of restaurants during peak hours. In doing so, this study provides answers to the following research questions:
- What is the mean noise level in restaurants in Hong Kong?
- Is it true that Chinese restaurants are noisier than other types of restaurants in Hong Kong as claimed by people? ,
- What are the contribution factors of high noise levels in restaurants?
- How likely will the noise exposure experienced by restaurant service employees exceed the Hong Kong occupational noise limit of 85 dBA daily workplace noise exposure level?
Answering these questions can shed light on the possible reduction of indoor noise level in restaurants that bring a positive effect on psychological and physiological health of service employees as well as customers.
Restaurant noise and its mathematical model
Restaurant noise has been frequently mentioned in mass media ,, and the effect of background noise on consumer behaviors in restaurants has been widely studied. ,,,, However, restaurant noise has yet to be systematically quantified and the factors contributing to it have yet to be properly identified. One of the earliest studies on restaurant noise was conducted by a group of otolaryngologists in the US  who were primarily interested in the effect of noise on conversations in restaurants, in particular for those with hearing-impairment. Lebo et al.  reported that background noise levels in restaurants including Bistro, California cuisine, ethnic cuisine, steakhouse, and fast food ranged from 65 to 80 dBA, while that in elegant restaurants ranged from 60 to 66 dBA. They indicated that the critical background noise level for speech discrimination was between 65 and 70 dBA and any noise level exceeding this level caused interference in communications, for both normal and hearing-impaired people. Zemke et al.  performed noise measurements at 15-min intervals in a causal Mexican restaurant in the US and found that noise levels ranged from 55.3 to 74.5 dBA. Long  indicated that only one of the top 100 restaurants in the San Francisco area had a background noise level below 65 dBA and presented a mathematical model on the reverberant sound field in restaurants. Samagwa et al.  measured noise levels in restaurants in Morogoro, Tanzania. They reported that the measured noise levels ranged from 61 to 64 dBA. Yu and Wong  carried out noise measurements in Chinese restaurants and performed audiometric tests on restaurant employees. They reported that the noise levels were highest in kitchens (mean = 87 dBA, SD = 5.7 dBA), followed by dish washing area (mean = 82.5 dBA, SD = 3.6 dBA) and service area (mean = 75.9 dBA, SD = 5.6 dBA). Yu and Wong  estimated that 47% of Chinese restaurant employees were exposed at or above the first action level of LEP,d 85 dBA (daily noise exposure level) as stipulated in the Hong Kong Noise at Work Regulation. The audiometric test results showed that restaurant employees had noise-inducing hearing loss, with a dip of 30-35 dB at 6 kHz. Ryberg  studied the sound pressure levels produced by music in concert halls, pubs, restaurants, discotheques, and cinemas in Sweden. She reported that 19% of the measured noise levels in pubs and restaurants exceeded 100 dBA Leq,1-h .
When a restaurant patron sits in a restaurant, s/he is exposed to two types of sound: One is the speech from her/his friend, and another is the background noise emanated from all other sources including multiple talkers in restaurants, activities and/or background music. In Hong Kong, background music is uncommon and hence that background noise is dominated by the reverberant noise from activities and other patrons. To model such a situation, the direct sound from a talker can be characterized by Eq. 1.
where SPLdirect is sound pressure level due to the direct sound from the talker (dB re 2 10−5 Pa), SWLtalker is sound power level of the talker (dB re 10−12 W; 70 dBA for typical conversation), r is the distance between the talker and listener and Q is source directivity (2 for on-axis voice).  The listener is also exposed to the reverberant sound emitted from all sources in a restaurant as given in Eq. 2.
where SWLall sources is the total sound power of sources, and RC is the room constant in . S is the total surface area of the restaurant in m 2 and αm is average absorption coefficient in the restaurant. When noise measurements are taken place in a restaurant, noise meter must be positioned in a place that is predominantly characterized by the reverberant noise as shown in Eq. 2. Hence, the background noise can be represented by the following equation:
where the total number of occupants is equal to , ρseating is the seating density, ρoccupancy is the occupancy density ranging from 0 to 1 (i.e., 0-100%), and N is the number of persons per table. Normally, one of N companions will talk over the table once at a time. Hence, Eq. 3 is re-written as below.
It should be noted that Hong Kong's rental cost is extremely high and all restaurants have adopted the limit of 1.5 m 2 per person as ρseating in their design  and N is typically 4 (as observed during noise measurements). Again, SWLtalker for a talker is approximately equal to 70 dBA, but in this study, SWLtalker was treated as a variable depending on the occupancy density, meaning that a talker changes her/his voice level when the occupancy density changes.
| Methods|| |
A total of 50 restaurants were selected from the Hong Kong Yellow Pages using random sampling. The owners or general managers of these restaurants were then approached. After understanding the purpose of our work, 12 of them agreed to participate in the study. Among these 12 restaurants, 5 of them were Chinese restaurants, 3 were fast food restaurants, and 4 were Western restaurants. The owners or managers of other restaurants declined our invitation due to their concern about interfering with business. Noise measurements were conducted during peak hours in the breakfast, lunch and dinner times in 4 Chinese and all fast food restaurants, and during peak hours in the lunch and dinner times in 1 Chinese and all Western restaurants.
A Type-1 precision integral sound level meter (B&K 2236) was used to record Leq,1-h noise level in dBA. The sound level meter was calibrated by a calibrator (B&K 4231) before and immediately after each noise measurement. All measurements were considered as acceptable as the calibration levels were all within 1.0 dBA before and after each measurement. In total, 31 sets of noise measurements were recorded. During noise measurements, the geometrical properties of each restaurant including its floor area, and nominal height were also recorded. The occupancy density was categorized as very low (<40%), low (40-59%), medium (60-79%), and high (80-100%) because it varied slightly and continuously in many of the selected restaurants. It was observed that the occupancy density ranged from low to high during peak hours, and all restaurants had carpeted floors, tables covered with tablecloths, polyvinylchloride (PVC) leather dining chairs, and suspended ceilings at a height of 2.4-4.5 m.
| Results and Discussion|| |
Among 31 sets of the recorded noise levels, 14 of them were recorded in Chinese restaurants, 9 were recorded in fast food restaurants, and 8 were recorded in Western restaurants. [Table 1] shows the measured noise levels in restaurants. The Leq,1-h noise levels ranged from 67.6 to 79.3 dBA (mean = 73.88 dBA, SD = 3.70 dBA) for Chinese restaurants, from 69.1 to 79.1 dBA (mean = 74.08 dBA, SD = 3.74 dBA) for fast food restaurants, and from 66.7 to 82.6 dBA (mean = 73.91 dBA, SD = 4.55 dBA) for Western restaurants. In other words, the mean noise level in all these restaurants was 73.95 ± 1.40 dBA at a 95% confidence level. This finding was consistent with the noise level presented by Yu and Wong.  The mean noise levels in Chinese, fast food, and Western restaurants were 73.88, 74.08, and 74.08 dBA, respectively. When noise measurements were grouped by time periods, the mean noise levels measured during the breakfast, lunch, and dinner times were 74.19, 74.83, and 72.92 dBA, respectively. When noise measurements were grouped by the occupancy density, the mean noise levels measured at low occupancy density (40-59%), medium occupancy density (60-79%), and high occupancy density (80-100%) were 68.85, 72.75, and 76.51 dBA, respectively. These values provide the answer for the first research question.
The analyses of variance were performed on the data grouped by "types of restaurants", "time periods", and "levels of occupancy density", respectively. Results show that there was no significant difference in the measured noise levels between different types of restaurants and no significant difference in the measured noise levels between different time periods. The findings show that Chinese restaurants were not noisier than other types of restaurants in Hong Kong. However, the measured noise levels at different levels of occupancy density were statistically different (P < 0.01). In particular, the mean noise level in restaurants with high occupancy density was 76.51 dBA. This was significantly higher than the mean noise level in restaurants with medium occupancy density at a level of 72.75 dBA and low occupancy density at a level of 68.85 dBA.
Bivariate correlations between the values of measured noise level, floor area, height, and occupancy density (as an ordinal variable) were calculated. [Table 2] presents the calculated Pearson's correlation coefficients. It shows that the associations between the measured noise level and floor area and between the measured noise level and height were very weak and insignificant (r <ǀ−0.22ǀ, P > 0.05), while the association between the measured noise level and occupancy density was very strong and significant (r = 0.703, P < 0.001).
|Table 2: Pearson's correlation between noise level, floor area, height, and occupancy density|
Click here to view
A stepwise multiple regression analysis was performed to assess the contributions of "floor area", "height", "occupancy density", "type of restaurant (using two dummy variables)", and "time period (using two dummy variables)" on the measured noise level. Result shows that "occupancy density" was the only significant contributing factor influencing the measured noise level in a restaurant. The resulting equation is given below:
where Di is the coefficient for different occupancy densities (Di = 1 for low occupancy density; Di = 2 for medium occupancy density; and Di = 3 for high occupancy density). The R2 of Eq. 5 was 0.49, indicating that 49% of the variance in noise level was caused by the level of occupancy density. [Figure 1] presents the predicted versus measured noise levels. The variation in each group of the measured noise levels seemed to be quite large, probably due to operational factors such as how foods were ordered and served and human factors such as mood and atmosphere varied among restaurants.
|Figure 1: The predicted noise levels using Eq. 5 versus the measured noise levels|
Click here to view
Reconciliation between the measured noise levels and the mathematical model
Eq. 4 shows that background noise level in a restaurant depends on a number of parameters including the room constant RC . The RC in turn depends on the total surface area and average absorption coefficient αm that is determined from the following equation:
where αi is absorption the coefficient of the ith object such as floor and ceiling, or absorption coefficient per person, per PVC leather (unoccupied) dining chair, and per table. As the walls of restaurants were hard and sound reflective, the absorption coefficient of walls was set to zero. The right hand side of Eq. 6 shows that αm is proportional to floor area and the total absorption coefficient/m 2 in the restaurant (say, αsum ) that takes noise absorption from floor, ceiling, per person/m 2 , per chair/m 2 , and per table/m 2 into consideration, and is inversely proportional to the total surface area of the restaurant S. In fact, S is expressed as:
where h is the nominal height of the restaurant in m and Lperimeter is the perimeter of the restaurant in m. Substituting Eq. 7 into Eq. 6, one can obtain the following equation:
Substituting Eqs. 7 and 8 into Eq. 4 and simplifying, one can obtain Eq. 9.
In most restaurants, the value of (2 floor area) was much greater than the value of (h Lperimeter ). Hence, Eq. 9 is written as:
Eq. 10 indicates that background noise level in restaurants primarily depends on the occupancy density only. It is consistent with the empirical findings as shown in Eq. 5. However, as what was stated before, SWLtalker might depend on the occupancy density. Assuming that at the mid frequency of 500 Hz, the absorption coefficient of carpeted floor was 0.3, that of plaster normal suspended ceiling was 0.1, that of per person/m 2 (assuming full occupancy) was 0.267 (=0.4-1.5), that of per chair/m 2 (if unoccupied) was 0.1 (=0.15-1.5), and that of per table/m 2 was negligible.  The value of αsum for full occupancy was then calculated as 0.667. In addition, as N is 4 and αoccupancy is 1, Eq. 10 becomes:
where C is -1.76 dBA for full occupancy. The value of αsum for other levels of occupancy and thus SPLbackground for other levels of occupancy were determined. [Table 3] shows the value of C as a function of occupancy. Assuming that high occupancy density was 0.9, medium occupancy density was 0.7, and low occupancy density was 0.5 and using the value of C from [Table 3] as well as Eqs. 10 and 5, it was found that the voice level of a talker at the low occupancy density environment was 72.83 dBA. The voice level of a talker was 75.53 dBA at the medium occupancy density environment. The voice level of a talker was 78.58 dBA at high occupancy density. Hence, the voice level of a talker increased when the occupancy density (and thus background noise level) increased.
|Table 3: C as a function of the occupancy density from 0.4 to 1 (i.e., 40-100%)|
Click here to view
There were some uncertainties associated with the values of occupancy density and the measured background noise levels ranged from 70.1 to 82.6 dBA for restaurants with high occupancy density. Hence, Monte Carlo simulations were performed 100 times, using random selection of the measured background noise levels from high occupancy scenarios and an uniform distribution between 0.8 and 1.0 for the occupancy density. Using Eq. 10, the mean voice level of a talker was found to be 78.19 dBA with a 95% confidence interval of [77.55 dBA, 78.83 dBA] at high occupancy density. Monte Carlo simulations were also performed 100 times each for the medium occupancy density and the low occupancy density situations. Results show that the mean voice level of a talker was 75.53 dBA with a 95% confidence interval of [75.16 dBA, 75.90 dBA] at medium occupancy density, and 72.77 dBA with the 95% confidence interval of [72.36 dBA, 73.18 dBA] at low occupancy density, respectively. It should be noted that the coefficients given in [Table 3] shall be applied to restaurants in other cities with great care. It is because variables including patrons per table and seating density vary across cities.
| Conclusion|| |
Noise in restaurants affects customers and service employees. When people enjoy their meals and talk to friends over the table, noise is particularly annoying. For service employees, they are exposed to relatively high level of background noise continuously during peak hours, sometimes 8 h a day.
This paper presents a noise survey in different types of restaurants in Hong Kong during peak hours in the breakfast, lunch, and dinner times. Results show that the measured noise levels using Leq,1-h ranged from 67.6 to 79.3 dBA with the mean at 73.88 dBA in Chinese restaurants, from 69.1 to 79.1 dBA with the mean at 74.08 dBA in fast food restaurants, and from 66.7 to 82.6 dB(A) with the mean at 73.91 dBA in Western restaurants. Results of the analysis of variance show that there were no significant differences between means of the measured noise levels among different types of restaurants, indicating that Chinese restaurants were not noisier than other types of restaurants. However, background noise level depended primarily on the occupancy density as evidence from the results of the analysis of variance grouped by the occupancy density and stepwise multiple regression analysis. The paper presents a mathematical model and validates the model using empirical data. For a particular restaurant, the model equation given in Eq. 10 Shows that increasing seating area per person and absorption coefficient per unit area can reduce background noise level. Hence, the restaurant manager has to strike a balance between the occupancy (including the occupancy density and seating area per customer) and a good acoustic environment. By comparing the maximum measured hourly noise level in restaurants at 79.3 dBA to the regulated daily noise exposure level of 85 dBA, it is not likely that Hong Kong's restaurant service employees will be exposed to the noise level that can cause occupational hearing loss, unlike their counterparts who work in kitchens. 
| References|| |
The US National Restaurant Association. 2013 Restaurant Industry Pocket Factbook. Washington, USA: National Restaurant Association; 2013.
The UK Department of Environment, Food and Rural Affairs. Food Statistics Pocketbook 2012. London, UK: Department for Environment, Food and Rural Affairs; 2013.
Hong Kong Census and Statistics Department. Key Statistics on Business Performance and Operating Characteristics of the Import/Export, Wholesale and Retail Trades, and Accommodation and Food Services Sectors in 2010. Hong Kong, China: Hong Kong Census and Statistics Department; 2011.
Webster T. Occupational hazards in eating and drinking places. Compens Work Cond 2001;4:27-33.
Tsai JH. Chinese immigrant restaurant workers′ injury and illness experiences. Arch Environ Occup Health 2009;64:107-14.
Gaydos M, Bhatia R, Morales A, Lee PT, Liu SS, Chang C, et al.
Promoting health and safety in San Francisco′s Chinatown restaurants: Findings and lessons learned from a pilot observational checklist. Public Health Rep 2011;126 Suppl 3:62-9.
Jones S, Love C, Thomson G, Green R, Howden-Chapman P. Second-hand smoke at work: The exposure, perceptions and attitudes of bar and restaurant workers to environmental tobacco smoke. Aust N Z J Public Health 2001;25:90-3.
Liu RL, Yang Y, Travers MJ, Fong GT, O′Connor RJ, Hyland A, et al.
A cross-sectional study on levels of second-hand smoke in restaurants and bars in five cities in China. Tob Control 2010;19 Suppl 2:i24-9.
McNabola A, Eyre GJ, Gill LW. Environmental tobacco smoke in designated smoking areas in the hospitality industry: Exposure measurements, exposure modelling and policy assessment. Environ Int 2012;44:68-74.
Santos CY, Neto FA, Cardoso JN. Volatile organic compounds: Distribution in offices and restaurants in the cities of Rio de Janeiro and São Paulo (Brazil). Indoor Built Environ 1997;6:168-73.
To WM, Yeung LL, Chao CY. Characterisation of gas phase organic emissions from hot cooking oil in commercial kitchens. Indoor Built Environ 2001;9:228-32.
To WM, Lau YK, Yeung LL. Emission of carcinogenic components from commercial kitchens in Hong Kong. Indoor Built Environ 2007;16:29-38.
Yeung LL, To WM. Size distributions of the aerosols emitted from commercial cooking processes. Indoor Built Environ 2008;17:220-9.
Jayaraman S, Dropkin J, Siby S, Alston LR, Markowitz S. Dangerous dining: Health and safety in the New York City restaurant industry. J Occup Environ Med 2011;53:1418-24.
To WM, Ip RC, Lam GC, Yau CT. A multiple regression model for urban traffic noise in Hong Kong. J Acoust Soc Am 2002;112:551-6.
Ko JH, Chang SI, Kim M, Holt JB, Seong JC. Transportation noise and exposed population of an urban area in the Republic of Korea. Environ Int 2011;37:328-34.
Ising H, Kruppa B. Health effects caused by noise: Evidence in the literature from the past 25 years. Noise Health 2004;6:5-13.
Mahendra Prashanth KV, Venugopalachar S. The possible influence of noise frequency components on the health of exposed industrial workers - A review. Noise Health 2011;13:16-25.
Bohanon HR Jr, Piadé JJ, Schorp MK, Saint-Jalm Y. An international survey of indoor air quality, ventilation, and smoking activity in restaurants: A pilot study. J Expo Anal Environ Epidemiol 2003;13:378-92.
Chan DW, Tam CS, Jones AP. An inter-comparison of VOC types and distribution in different indoor environments in a university campus. Indoor Built Environ 2007;16:376-82.
Delgado-Saborit JM, Stark C, Harrison RM. Carcinogenic potential, levels and sources of polycyclic aromatic hydrocarbon mixtures in indoor and outdoor environments and their implications for air quality standards. Environ Int 2011;37:383-92.
Lee SC, Li WM, Chan LY. Indoor air quality at restaurants with different styles of cooking in metropolitan Hong Kong. Sci Total Environ 2001;279:181-93.
Liu Y, Chen R, Shen X, Mao X. Wintertime indoor air levels of PM10, PM2.5 and PM1 at public places and their contributions to TSP. Environ Int 2004;30:189-97.
Lo KC. Chinese Face/Off: The Transnational Popular Culture of Hong Kong. Hong Kong, China: Hong Kong University Press; 2005.
Tarn SM. Lost, and found?: Reconstructing Hong Kong identity in the idiosyncrasy and syncretism of yumcha. In: Wu DY, Tan CB, editors. Changing Chinese Food Ways in Asia. Ch. 3. Hong Kong, China: The Chinese University of Hong Kong; 2001. p. 49-69.
Milliman RE. The influence of background music on the behavior of restaurant patrons. J Consum Res 1986;13:286-9.
North AC, Hargreaves DJ. The effects of music on responses to a dining area. J Environ Psychol 1996;16:55-64.
North AC, Shilcock A, Hargreaves DJ. The effect of musical style on restaurant customers′ spending. Environ Behav 2003;35:712-8.
Wilson S. The effect of music on perceived atmosphere and purchase intentions in a restaurant. Psychol Music 2003;31:93-112.
Novak CC, Lopa JL, Novak RE. Effects of sound pressure levels and sensitivity to noise on mood and behavioral intent in a controlled fine dining restaurant environment. J Culinary Sci Technol 2010;8:191-218.
Lebo CP, Smith MF, Mosher ER, Jelonek SJ, Schwind DR, Decker KE, et al.
Restaurant noise, hearing loss, and hearing aids. West J Med 1994;161:45-9.
Zemke DM, Hertzman JL, Raab C, Singh D. A little more noise and a little less conversation? Ambient noise in restaurants. J Foodserv Bus Res 2011;14:256-71.
Long M. Dinner conversation (an oxymoron?). Acoust Today 2005;1:25-7.
Samagwa D, Mkoma SL, Tungaraza C. Investigation of noise pollution in restaurants in Morogoro Municipality, Tanzania, East Africa. J Appl Sci Environ Manag 2009;13:29-33.
Yu TS, Wong TW. Occupational Noise Exposure and Hearing Impairment Among Employees in Chinese Restaurants and the Entertainment Sector in Hong Kong. Hong Kong Health Services Research Committee Report# 621016, Hong Kong; 2002.
Ryberg JB. A national project to evaluate and reduce high sound pressure levels from music. Noise Health 2009;11:124-8.
Hong Kong Food and Environmental Hygiene Department. A Guide to Application for Restaurant Licenses. Hong Kong, China: Hong Kong Food and Environmental Hygiene Department; 2012.
Smith BJ, Peters RJ, Owen S. Acoustics and Noise Control. 2 nd
ed. London, UK: Longman; 2000.
Prof. Wai Ming To
Macao Polytechnic Institute, Rua de Luis Gonzaga Gomes, Macao SAR
People's Republic of China
Source of Support: None, Conflict of Interest: None
[Table 1], [Table 2], [Table 3]
|This article has been cited by|
||A Conceptual Framework for Acoustic Comfort Classification in Eatery Places: Critical Reviews of the Determining Factors
| ||Noor Aini Mistar, Raha Sulaiman, Nazli Bin Che Din |
| ||Acoustics Australia. 2020; 48(3): 337 |
|[Pubmed] | [DOI]|
||Effect of children on the sound environment in fast-food restaurants
| ||Qi Meng, Shanshan Liu, Jian Kang |
| ||Applied Acoustics. 2020; 162: 107201 |
|[Pubmed] | [DOI]|
||Conceptual framework on noise ranking classification in eatery places for human psycho-acoustics preferences towards acoustic comfort
| ||Nazli Che Din, Noor Aini Mistar, Raha Sulaiman, Zunaibi Abdullah, Musli Nizam Yahya, Zaiton Haron |
| ||IOP Conference Series: Materials Science and Engineering. 2020; 849(1): 012001 |
|[Pubmed] | [DOI]|
||Noise in the work environment in the south moravian and moravian-silesian regions
| ||Eva Mrázková, Kristýna Vojkovská, Monika Micánková, Evald Záthurecký, Martina Kovalová |
| ||Profese online. 2019; 12(1): 1 |
|[Pubmed] | [DOI]|