PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Characterizing Community Noise in Hospital
Chiung Yao Chen
Chaoyang University of Technology, Taiwan, ROC

ABSTRACTThe present study was conducted in the lobbies of 16 Taiwanese urban hospitals to establish what
contributes to the degree of noisiness experienced by patients and those accompanying them. Noise levelmeasurements were conducted by 15 min equivalent sound pressure levels (LAeq, 15m, dB). Statisticalattribute was better correlated, but the measured data was concerned whether the noise levels are normally
distributed or not. According to the correlation between subjective and objective surveys, the 3 independent
variables shown to have the largest effects on perceived noisiness were 1) Lmin – Lmax, 2) effective duration
RIWKHQRUPDOL]HGDXWRFRUUHODWLRQIXQFWLRQ Ĳe, h), of all LAeq, 15 m over 9 – 17, and 3) gradient of cumulative
distribution function (0.3 - 0.7 cumulative rate range). As an advanced approach, we found that the gradientof cumulative distribution function evaluates noisiness with more data statistical integrity than that was easily
attained by percentage levels.
Keywords: noisiness, hospital, loudness, autocorrelation function, cumulative distribution function

1. INTRODUCTION
1.1 Noise events in the hospital lobby
The noise in hospitals is detrimental to the recovery of patients, who need an environment quieter thanthey usually experience in their daily lives. Nevertheless, verbal communications in a hospital lobby
constitute a necessary behavior at registration desk, dispensary counters, and outpatient service counters.
Those actual procedures that patients undergo normally lasting longer than 20 – 30 min in the hospital
lobbies can be an uncomfortable experience in Taiwan. This study focuses on noise-related variables inheavily trafficked hospital lobbies including noise levels at registration desk, at dispensary counters and in
emergency departments. The noise sources in hospital lobbies consist primarily of people, machinery, andmedical appliances; however, the specific acoustical characteristics of hospital lobbies have not been wellestablished.
The daily activities in hospital have resulted in excessive noise generated from sources such asenormous air-conditioners, medical treatments involving noisy portable carts, conversations, and even TV
sounds in lobbies. Recently, many hospitals in Taiwan have opened a convenience store in their lobby, thus
greatly increasing the complexity of the noise profile in these places. Furthermore, hospitals run hugemechanical infrastructures, such as the magnetic resonance imaging (MRI) and computerized tomography(CT) medical facilities. For convenience, to dampen their sounds and vibrations, and for the purposes of
emergencies, these pieces of equipment have routinely been sited on the first floor (1F) or basement floor(BF). Such facilities, however, drastically augment noise over a frequency range of 500 – 2000 Hz
measured in operating rooms and with spectral peaks of 250 Hz (Mcjury, 1995). However, in our previous
survey, the noise levels of these magnet facilities were under 49 LAeq at 1–min intervals measured at the
hallway right outside of these examination rooms.
Many studies have provided any information about annoyance and noise perceptions in terms ofroad-traffic composition (Sandrock et al., 2010; Abo-Qudais, Abu-Qdais, 2005). Those studies concluded
that annoyance is highly dependent on the actual noise sources in complex traffic scenarios but provided
only limited evaluations regarding the complicated hospital noise disturbances in the lobbies studied. Thisis an important issue in hospital lobbies because the most of information is transmitted orally (for example,
by nurses or other staff members speaking with patients and their families). In this study, we investigatedthe psychological effects of noise in 16 urban hospital lobbies with the aim of providing complicated noise
evaluations based on a variety of independent annoyance descriptors used in previous works.
1.2 Noise Criteria
Previous studies have employed equivalent sound pressure level (Leq) measurements to quantify noiselevels according to the WHO guideline recommendations (Berglund et al., 1999, page 7). Some studies
have focused on the variation of Leq over a 24-hour period (Bush-Vishniac et al., 2005; Yamada et al.,7847

2003; Orellana et al., 2007; Kracht et al., 2007; Williams et al., 2007). For road-traffic noise, Griffiths,
Langdon (1968), proposed using a statistical level of LN, with L10 – L90 as the measure of variability. De
Coensel et al. (2005), used L5 – L95 as an effective descriptor of noise observations since they showed much
more variation among statistical levels, while Torija et al. (2010), used a sound level variance which was
calculated by the standard deviation of the sound levels. However, Torija et al. (2010), provided only
limited psychological research regarding their models. These indicators provide an idea of the cumulative
fluctuation levels from the average background noise. In addition, different noise annoyance ratings for thesame LAeq were caused by different mean percentile loudness in sone (N10 or N5). They concluded that the
average loudness, N, better correlates to annoyance ratings than the percentile value of loudness N5 and that
N can be used a noise index for time-varying noise. However, their four traffic noise structures wereartificially created in a laboratory from single vehicle pass-by recordings, or we found that their noisinessresearch subjects were mostly subjected to specific individual noise sources. They do not seem capable ofrepresenting community noise, which is defined by WHO (Berglund et al., 1999, page 55) as noise emitted
from all sources, including road-traffic, industries, construction, public work, and neighborhoods; i.e., thereal noises which occur in our environments surrounding us. Whereas the findings reported by Kaczmarek
and Preis (2010) regarding their experiences of the variance in road-traffic noise measures, we believed thatstatistical levels and percentile loudness of noise measurements would serve as an effective descriptor of
the psychological noisiness for the complicated noise sources in hospital lobbies, too. Other measures,
meanwhile, such as the maximum and minimum noise levels, octave band levels, and, in particular, thesense of time-varying in noise levels continuity have been investigated by using autocorrelation function
analysis (Ando, Chen, 1996; Sato et al., 2007).
In the early stage of measurements, the day-night noise in hospitals, unpredictable impulse noise, which
blends into background noise, often poses the biggest difficulties for monitoring background noise in
hospitals. However, it was identified that the noise level in general hospital wards is a continuous steadystate and varies in daytime (9 -17) are normally distributed. This explains why noise events were averaged
at 15-min intervals in this study to detect the noise events without an impulse wave. The frequency ofoccurrence and the sound pressure level of multiple impulse noise events can easily be assessed throughtheir cumulative percentile sound pressure level. Such impulse noises include ambulance sirens, humanscreams, and construction noise. In Figures 1, the cumulative percentages of daytime (9 -17) noise eventswere normally distributed for both indoor and outdoor areas. Sporadic impulse noise events, marked byhigh decibel levels in the nighttime (17-24), are located on the right side of these distributions (where D >0.90).

Fig. 1. A comparative cumulative rate of the levels of LAeq at each 15-min interval over the daytime (9-17)
and the nighttime (17-24) in the 16 surveyed hospital lobbies.
1.3 Psychological noisinessYamada et al. (2003), performed post-occupancy evaluations of subjective responses to soundenvironments in hospital wards. Their analysis of a questionnaire survey showed that many inpatients
pointed out that conversations and noisy medical equipment measured by LAeq at 15 min intervals wererated as unacceptable noises. To clarify the relationship between the objective measures and the subjective
response to complex noise, a questionnaire survey and noise measurements were conducted in the lobbiesof 16 urban hospitals in Taiwan. To measure the level of noisiness, visitors in the lobbies were asked to fillout questionnaires, since it would be difficult to evaluate such noisiness via the professional roles of theindividuals working in hospitals (Ryherd, 2008; Yamada et al., 2003; Tsiou et al., 2008).

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2. METHODOLOGY
2.1 Hospital Survey
16 urban hospitals located in various Taiwanese cities, all of which included service departments such as
internal medicine, surgery, orthopedics, cardiology, etc., were selected to participate in the survey. Each ofthese hospitals had outpatient services, an emergency department, examination rooms, operating rooms
connected to their lobbies, and wards with more than 100 beds. However, hospital regulations in Taiwan
require that inpatient wards be located as far from the lobbies as possible; hence, those wards were located indifferent buildings to the extent possible according to the size of the overall hospital site. However, most of
the hospitals sited their ward departments in the same building above the lobbies. Meanwhile, hospitals
hosting medical schools were excluded from this study, because their service departments, especially theiremergency departments, were sited in separate buildings. Each of the hospitals surveyed is confined to asingle site; large-sized hospitals consisting of multiple buildings were not included. The main entrance ofeach surveyed hospital is adjacent to a main road with a width of more than 30 m, and the distance from thehospital façade to the road in each case is less than 6 m. The main entrances open into the lobby, all of whichhave outpatient registration and dispensary counters. Some of the hospitals surveyed have a drug orconvenience store adjacent to the lobby.
2.2 Questionnaire survey of noisiness
Based on the information above, information regarding several subjective responses was requested in thesurvey. First, do patients or their companions located in the lobby perceive the place to be noisy? And if so,
are the noise sources grouped by a factor analysis according to the hospital’s routine activities? Also, is it
possible for these surveyed lobby visitors to rank the perceived noisiness of one or even all of these
activities? A five-point Likert scale was offered in the questionnaire to those surveyed to assess the level ofnoisiness (Parasuraman et al., 1991). The subjects were asked to evaluate twelve audible events inside thehospital. The total number of subjects was 584. For each hospital, at least 28 people participated in thesurvey. The subjective responses obtained by this questionnaire survey were classified for noisinessaccording to definitions established by Berglund et al., (1975). The arithmetic means of noisiness were
calculated using the subjective responses from the subjects in each hospital’s lobby and averaging thefive-point scales for the 12 noise events.
Figure 2 shows the noisiness results with the standard deviation (SD, noted by an error bar). The SD
values were calculated for the individual responses at each of the 16 hospital; noisiness variance differed
between hospitals, even in those with an equal noisiness rating (e.g. hospital J and K). The results of analysis
of variance (ANOVA) tests showed significant differences in mean noisiness between individual subjects
across hospitals (F1, 15, 0.05 = 3.72, p < 0.001). The ANOVA results also showed significant differences
between the mean noisiness of the 12 noise events across hospitals (F1, 15, 0.05 = 4.85, p < 0.001). The meannoisiness of the 12 noise events associate well with factor loading in the factor analysis results ((Kaiser Meyer - Olkin (KMO) = 0.849, n = 584)). Reliability analysis (Cronbach’s alpha = 0.834 in primary noise,PN; 0.746 in accidental noise, AN) were conducted to test the reliability and internal consistency of eachfactor. The factor loading of 12 individual audible events were construction activity (0.779, AN), children
playing (0.774, AN), facility (0.769, PN), board casting (0.761, PN), medical appliances (0.740, PN), rollcall (0.696, PN), traffic (0.668, AN), entrance (0.639, PN), conversation (0.572, AN), TV (0.524, AN),ambulance (0.497, PN) and footfall (0.307, AN). These results indicate that the subjective noisiness scale use
in this study for the 12 noise events between 16 hospitals was statistically reliable in expressing theexperienced annoyance rating.

Fig. 2. The noisiness with their standard deviation of each hospital according to the questionnaire
responses of patients and those accompanying them measured at that lobbies.

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2.3 Noise measurements
To measure the noise levels in the hospital lobbies, A-weighted equivalent sound pressure levels (LAeq, T)
using time averages at 15-min intervals were obtained over the daytime period of 9 – 17 and nighttime of
17-24. Two microphones (Brüel and Kjær, B&K type 4190) with a fast time constant (0.125 s) were used torecord noises at two receiver positions. The first position was in the center of the given lobby’s visitor
seating area at least 1 m away from the walls and at a height of 1.2 m, with the goal of recording all thesounds that would be experienced by a visitor surrounded by the lobby’s soundscape. The other microphone
was located outside of the lobby’s main entrance and played the role of a road-traffic noise detector. During
all the measurements, the patients, staff members, and patients’ companions continued with their normal
activities. Data were collected with B&K PULSE system and corresponding analysis was conducted using
B&K 2-CHs. CPB analyzer software.

Fig. 3 Logarithmic average LAeq values for the 16 hospital lobbies (for indoor and outdoor measurements).
The vertical bars indicate the respective daily ranges.
2.4 Repetitive feature of noise levels
To evaluate the disturbances caused by the various hospital noises, the normalized ACF (NACF)
effective duration of noise levels (LAeq, 15 m), denoted by We values (h), was calculated to detect noise
variation in this study. The autocorrelation function (ACF) is defined by
1 T
lim
)p W
p ' t p ' t  W dt ,(1)
T of 2T ³T
Where p’(t) = p(t)s(t), in which p(t) is the sound pressure and s(t) is the ear sensitivity. For convenience,s(t) may be chosen as the impulse response of the A-weighted network. The value Ĳ represents the timedelay (h), and the value of 2T is the integration interval. The normalized autocorrelation function (NACF)
is expressed by(2)
M W ) (W ) / ) 0 ,pplog )p(W)) [dB]

Where ĭ  represents the ACF at delay time Ĳ as the maximum ĭ W). The We values were defined by
the ten-SHUFHQWLOHGHOD\ DWíG% REWDLQHGSUDFWLFDOO\IURPWKHGHFD\UDWHH[WUDSRODWHGin the range from G% WR í G% RI WKH ORJDULWKPLF 1$&) PRGXOXV (see Appendix). Namely, the We values (h) were
calculated with a sampling rate of 0.25 hours against alpha rhythm of the brainwave signals, 0.01 s.

Fig. 4. The noise levels of LAeq,15m in the hospital lobby (samples in D, H, L) shows an initial decline in theenvelope of the normalized autocorrelation function (NACF), and this decline can be fit to a straight line
regression in a range of 0 to -10 dB of the power of the NACF. The effective duration of NACF of noiselevels Ĳe, h) is defined as it crosses to -10 dB at that of delay.
Calculating We, NACF of all LAeq, 15 m over 9 – 17 in this study, only initial part of normalized ACF(approximately 0 to -10dB) showed the clearly decay for all data. As indicated in Figure 4, the value of We

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defined at the ten-percentile delay (-10 dB) is obtained by fitting the straight-line regression for (log()p(W))> - 10dB) of the ACF envelope for all. This procedure is similar to the manner of measuring the initial
reverberation time in room acoustics.

3. RESULTS
3.1 Equivalent sound pressure levels
The insulation loss (IL) value for each building in which the 16 hospital lobbies were located was
obtained based on the indoor position and outdoor position measurements of LAeq , 15m for each hospital
lobby, which are illustrated in Figure 3. The correlation coefficient between the IL value and the levelof psychological irritation was only -0.37, and the mean values of the indoor and outdoor test points
were still only 0.43. As indicated in Table 1, LAeq , 15m was correlated with noisiness, but othermeasures were more closely linked to noisiness, as explained by the correlation coefficient (r) and thestatistical reliability between the noisiness scale and the average LAeq , 15m values measured inside oflobbies, which was 0.46 (p = 0.07).
On the other hand, no other factor correlated to psychological noisiness could be detected from
Table 1. For example, the highest averaged noise levels were measured at hospital A, but the lobby inthat hospital has fewer connections with other functional spaces besides outpatient departments.
However, the architecture conditions of the hospital lobbies show that multiplications of each lobby’sarea, ceiling height, number of entrances and number of inpatient beds correlated roughly with thepsychological noisiness (r = 0.64, p < 0.01). We supposed, then, that the architecture conditions of thehospital lobbies would serve as a potential index of psychological noisiness with regard to soundintelligibility. As sound intelligibility was assumed to most affect noisiness, and intelligibility is inturn influenced by T30, the T30 values for each lobby were thus measured according to WHO
guideline (Berglund et al., 1999, page 56).
3.2 Statistical levels
It is necessary to move onto measures which relate to the variation of the sound pressure levels. The
trend of the correlation coefficients in Table 1 obviously increases as the N% values of the LN levels
rise. The variation of LAeq , 15m for all the measures throughout the daytime period (9 – 17) was found to
be more important than the averaged LAeq, 15m in the case of time-varying complicated noise
environments. As shown in Table 1, Lmin – Lmax, L95 – L5 , L90 – L10 , and the effective duration of thenormalized autocorrelation function Ĳe, NACF) of all the measurements made at 15-min intervals
were calculated and used as indices expressing the variation of the noise levels. The correlation
coefficients (r) between the noisiness scale and the Lmin – Lmax, L95 – L5 , L90 – L10, and Ĳe values were0.79 (p < 0.001), 0.74 (p < 0.01), 0.73 (p < 0.01) and 0.76 (p < 0.01), respectively. However, thecorrelation coefficients between the noisiness scale and the L95 – L5 in the nighttime was 0.39. Even if
the noise levels were not normally distributed, L95 – L5 value can be easily conducted. It denotes that
the LN level is not statistical integrity.
The variances in these sets of noise events reflect the noise concentration trend, which is thecloseness of the measured levels to the mean sound pressure levels (Figure5 (a)). Therefore, due tosimilar observation periods, the noise events measured at two different noise level concentration
trends can reflect the noise occurring rate in a certain area. The gradient of cumulative distributionfunction of the daytime noise events measured in 16 hospital lobbies was close to the mean value of
1.12 dB(A)/hr. It is clarifying that a linear curve in the Figure 5 (b) was observed for the results
measured in each hospital between the cumulative rate range of 0.3 to 0.7 (4.8hr). The gradientindicates the frequency of similar noise events in a certain space, or, in other words, the continuity ofnoise. For example, as shown in Figures 5, the difference in the mean noise levels of Hospitals A and
F was only 3.25 dB. However, there was a remarkable difference in their gradient of cumulative
distribution function, as the gradient of Hospital A was 1.38 times greater than that of Hospital F.

4. CONCLUSIONS
1. The noisiness ratings obtained from the questionnaire cannot be explained only by the average LAeq. As
shown in Table 1, the analysis showed that the noisiness ratings were well correlated withġthe Lmin andthe 90 percentile LAeq values. However, if the noise levels were not normally distributed, L95 – L5 value
can be easily conducted. It denotes that the LN level is not data statistical integrity.
2. Previous studies found that the We of brain waves correlates well to the subjective preference of musical7851

sound fields (Ando, Kageyama, 1977). Similarly, in this study, Ĳe values (h) calculated according to therange of noise levels indicated by L5 – L95 showed the highest correlation with noisiness (r = 0.89, R2 =0.77). This means that the initial part of autocorrelation of noise levels persistently reflects the noisinesswith regard to the estimation of ACF are almost equal to the real signal.
3. The gradient of cumulative distribution function between the cumulative rate of 0.3 to 0.7 (4.8hr), wasdefined as the gradient of time of measurement to the distribution of noise levels, as shown in Figure 5(b). It can serve as guidelines for future laws on the mandatory reduction of noise levels in hospitals.
The noise level statistics are strongly correlated with the conditions and activities in a certain space.
Therefore, the gradient of cumulative distribution function can be utilized as a condition for theestablishment of retail spaces in hospitals (not applicable to admission sections and pharmacies since it
is a requirement that they have to be located at the hospital lobby).
(a)

(b)

Fig.5 Probability distribution curves of daytime noise events in hospitals, measured hourly from 9 to 17(comparison of Hospitals A, B and F).

5. APPENDIX
Statistical implication in autocorrelation function of noise levels
The effective duration of NACF of the noise levels, We, was analyzed as a phenomenon of stationary
random processing (SRP). Concerning SRP for noise signal, the estimation of finite length data (2T) for theeffect of sound field must discuss a statistical error, and it has two conditions should be considered.
(1) The average values of signal P(t) are constant and independent within arbitrary time domain.
(2) The autocorrelation function (ACF) of signal is also independent in any time span, but only
associates with the distance (W) between two time position (t1,t2). And it equals to the expectation of timesquare average as a definition.

) p (t1 , t2 )E ( P(t1 ) P(t2 ))E ( P(t ) P(t  W )) ) p (W )(3)

Where )p (W) equal to Equation (1)But for a finite length data (N) will only obtain an estimation of ACF
Nˆ (W ) 1 ¦ p (n) p(n  W )
)
N
Nn1

(4)

And the real length of signal for calculation are N-W, thusˆ (W ))

1 N W¦ p N ( n) p ( n  W )
N n1

(5)

The expectation of error for estimating areˆ (W ) ºerror ª¬)
¼

7852

^

`

ˆ (W )  ) (W )
E )

(6)

Whereˆ (W ) ºerror ª¬)
¼

N W

1
¦ )(W )N n1

N W
) (W )
N(7)

Therefore, the expectation of error areˆ (W ) ºerror ª¬)
¼



W
N

) (W )(8)

The conclusion are,(1) When N closes to infinity, error will decrease to 0.
(2) As W<< N, the estimation of ACF are almost equal to the real one.
Table 1. Correlation coefficient (r) with p value results between noise criteria and subjective noisiness inlobbies
Item
Correlation (r)Fit (r2)
Noise level[dBA]
Average LAeq, 15m
0.46
0.22
LAeq, 15m standard deviation VLAeq-0.76 ***
0.58
Lmax
-0.13
0.02
Lmin
0.69 **
0.48
Insulation of building against traffic noise-0.37
Autocorrelation function (ACF)[h]
We of NACF in LAeq, 15m
0.76 **
0.58
Gradient of cumulative distribution function
0.76 *
0.59
Percentile LAeq values (LN)
[dBA]
L5
0.33
0.11
L10
0.56
0.31
L50
0.69 *
0.48
L90
0.71 **
0.51
Compounded indices
Lmin – Lmax
0.79 ***
0.62
L95 – L50.74 **
0.55
We /(L5 – L95)
0.89 ***
0.77
L90 – L10
0.73 **
0.53
Noise criteria
NC (Noise criterion) (ANSI, 2008)
0.41
0.17
RC (room criteria)
0.45
0.20
Average loudness, N[sone] (ISO532, 1975)
0.64 **
0.41
Percentile loudness, N5 [sone]
0.73 **
0.53
N10 [sone]
0.72 **
0.52
Sharpness (Aure)30s
0.45
0.20
Unbiased annoyance (UBA)30s(Zwicker, Fastl, 1999)
0.71 **
0.51
T30 (500Hz)[s]
0.56 *
0.30
Multiplication (architecture conditions)0.64 **
0.41
Notes: *** p < 0.001, ** p < 0.01, * p < 0.05

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