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

A pilot study in primary school on the effect of noise monitoring
system with lighting feedback on teachers’ voice parameter, noiselevels and subjective assessments
Sonja DI BLASIO1; Giuseppina Emma PUGLISI1; Chiara GERVASI2; Antonella CASTELLANA2;
Silvia MURGIA1; Greta MINELLI1; Giuseppe VANNELLI1; Simone CORBELLINI2; Alessio
CARULLO2; Arianna ASTOLFI1
1
2

Politecnico di Torino, Department of Energy, Italy

Politecnico di Torino, Department of Electronics and Telecommunications, Italy

ABSTRACT
Teachers’ vocal behaviour is affected, among other aspects, by the noise generated by pupils that talk andmove in classroom. Under noisy conditions, teachers are likely to increase their voice level causing possible
negative consequences on their vocal health. In this pilot study a noise monitoring system with a lighting
feedback, namely the SEM (Speech and Sound SEMaphore) device, has been used as an educational tool. It
encourages pupils to reduce their voice volumes through a coloured lighting feedback. Seven teachers fromfour classes of a primary school in Turin (Italy) have been monitored over two to four working days with
SEM switched on and off. The results have shown that the background noise level averagely decreased byabout 2.9 dBA when SEM was switched on. With SEM switched off, a tendency towards Lombard effect has
occurred, i.e. an increase in the speech level with background noise level at a rate of about 0.4 dB/dB.
Conversely, such effect has not been found with SEM switched on. Self-reported impressions of teachers
indicated that SEM effectively reduces vocal effort and improves speech intelligibility in the classroom.
Keywords: Vocal load, Noise, Classrooms

1. INTRODUCTIONClassroom acoustics may generate challenging environments for both students and teachers, as it
influences speech intelligibility and the required vocal effort in order to be understood, respectively.
Particularly, when poor acoustics is present it may degrade the students’ performance [1] and bringteachers to excessively raise voice with possible negative consequences on their vocal apparatus [2].
Together with the objective assessment of a classrooms’ quality and students’/teachers’
performances in it, there is an increasing observation of the subjective perception of the acoustic
environment by means of questionnaires. Particularly, questions on the perceived noise annoyance andon the most heard sound source in classrooms have brought to light that students’ activity is perceived
as the main source of noise in classrooms within the combination of different sounds. In a subjective
investigation on 51 classes, Astolfi et al. [3] found that student conversations and movements wereperceived as the highest noisy source in terms of intensity, frequency and disturbance in primaryschool classrooms. The accurate monitoring of classroom noise during the teaching activity istherefore fundamental, especially to evaluate the relationship between self-perceived annoyance andobjectively measured noise. As an example, Sato and Bradley [4] quantified that an average increase inbackground noise levels by about 10 dBA was generated by students during teaching activities.
In this framework, it is still unknown whether the involvement of occupants’ behaviour could
generate some effects on background noise levels and teachers’ voice status. An innovative method
based on a visual system with coloured lighting feedback has been applied to control noise levels
through the visualization of sound conditions encouraging lower students’ voice volume when noise1

sonja.diblasio@polito.it, giuseppina.puglisi@polito.it, giuseppe.vannelli@polito.it, arianna.astolfi@polito.it
chiara.gervasi@studenti.polito.it, antonella.castellana@polito.it, simone.corbellini@polito.it,
alessio.carullo@polito.it
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levels are elevated, however still on a small scale. Prakash et al. [5] pointed out that teachers, students
and management area (i.e. secretary deputy head teacher or head teacher) self-estimated the usefulnessof such a visual feedback system in school. The use of the SoundEar II device [6], led to improve theteaching process and classroom environment, as well as to reduce background noise levels in threeclasses involved in a short-term experiment [7]. Nonetheless, in order to increase the knowledge about
the effects of occupants’ behavior on learning and teaching process, research on the use of visualfeedback system as an educational tool is still required to provide evidence of its benefits in terms ofnoise reduction and vocal effort. Such aspect of voice use in relation with activity noise, in fact, stillneeds to be deepened with in-field, prolonged and repeated measurement campaigns that make use of
accurate tools too.
For this purpose, a pilot study was performed using a noise monitoring system with the lighting
feedback during long-terms voice monitoring in four primary school classrooms. The aim of this pilot
was to evaluate the extent to which the presence of a noise monitoring system could encourageteachers to reduce their vocal effort during plenary lessons due to a greater awareness of noise
conditions, and to reduce the background noise levels generated by positive pupils’ behaviour.

2. METHODOLOGY
2.1 Case studies
The present pilot study involved four classrooms belonging to a primary school placed in a
residential area of the center of Turin (Italy) that dates back to the nineteenth century. Due to theirlocation in the school building, classrooms either faced a road with low traffic or the internal courtyard,
therefore external noise is not a predominant source of disturbance. Geometry and finishes in theclassrooms are very similar; they all present large windows, earthenware tiles on the floors, highvaulted ceilings and acoustically treated walls, the latter consisting of plasterboard tiles (1.2 x 2.4 m,
with a percentage of perforation of 16%) with an air gap of 7.5 cm from the walls. The BS EN ISO
3382-1 standard [12], using the integrated impulse response method, was applied for measurements ofreverberation time (T 30 ) in the school classrooms, which had the average volume of 240 m3. The mean
reverberation time was 0.9 s in unoccupied condition and 0.6 s in occupied condition in the
mid-frequency range (from 0.5 kHz to 1 kHz). The acoustical renovation and measurements aredescribed in detail in Astolfi et al. [8].
2.2Monitoring of background noise levels

2.2.1 Device and procedure
A noise monitoring system with the lighting feedback, namely SEM (Speech and Sound
SEMaphore), was used to measure and control background noise levels during teaching activities inprimary school classrooms based on an adjustment of pupils’ behaviors, such as lowering voice
volumes. The SEM device has been developed, validated and patented at Politecnico di Torino.
The SEM device, which detailed description is given in Di Blasio et al. [9], consists in a class 2
sound level meter device (ISO-TECH SLM 52N) that measures background noise levels (LA90 ), and ina transparent panel illuminated by a through‐light beam. The lighting feedback alternates colours fromgreen, yellow and red according to the change of background noise levels.
The long-term noise monitoring was performed across three school years in three successive
campaigns using SEM devices located close to whiteboard and teachers’ desk in front of pupils in a
total of thirteen classes (four to five per year). The present work reports the methods and results related
to the third monitoring campaign in which, in addition to the use of the SEM device as an educational
tool, the teachers’ vocal activity and subjective assessments were evaluated. Overall, the monitoring
campaign was divided in phases according to lighting feedback of the SEM device, as follows: phaseone (P1), with SEM switched off and pupils unaware of the ongoing monitoring in order to be notinfluenced by an a-priori information; phase two (P2), with SEM switched on and pupils aware of theongoing monitoring, as they were informed of the relationship between the colours of the warning lightand the noise levels produced by themselves.
The background noise levels were recorded in both phases using the SEM device itself. Teachers
were asked to switch off and on SEM device and to note different variables in a daily logbook, e.g. typeof activity and the respective time-slot, own name, number of pupils, day of the week and possible
noise events coming from outside.

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2.3

Monitoring of teachers’ vocal activity

2.3.1 Subjects
One male and six female teachers have been monitored from two to four working days (3-4 hours
per each monitoring) over two different time periods according to P1 and P2 of the noise monitoring
campaign. Both phases of the long-term voice monitoring were carried out in the middle of the school
year with a distance from each other of four to six weeks. Teachers’ age ranged between 38 and 60
years, with a mean age of 48. Voice monitoring campaign involved four classes from first to fourth
grade (with pupils’ age from 5 to 9 years), where the number of pupils varied between 12 and 25
depending on the day and class. Table 1 shows the characteristics of the subjects and the total numberof long-term voice monitoring performed for each teacher according to the two monitoring phases.
Table 1 – Main characteristics of the classes and the teachers, and the total number of long-term voice
monitoring for each one, subdivided according to P1 and P2.
Classes

Teachers

Number of voice monitoring

Grade

Pupils’ number

ID

Age

Gender

P1

P2

IA

20-22

a

47

M

1

1

b

41

F

2

0

c

38

F

1

1

d

55

F

2

2

II B

12-14

II C

17-19

e

50

F

1

2

IV D

23-25

f

47

F

2

2

g

60

F

1

1

2.3.2 Recording equipment and procedure
The long-term voice monitoring was carried out using a portable vocal analyzer, based on Voice
Care technology, that has been developed at the Politecnico di Torino by Carullo et al. [10-12]. This
device consists of a Piezoelectric Contact Microphone (HX-505-1-1, Shenzhen, China), embedded ina collar, that has been placed near the jugular notch to sense the acceleration of the skin caused by thevibration of the vocal folds (Figure 1). The sensor is connected to a smartphone (Samsung
SM-G310Hn) where an application, the Vocal Holter App (by PR.O.VOICE, Turin, Italy), records thesignals with a sampling rate of 22050 Hz and 16 bit of resolution. The App estimates the Sound
Pressure Levels (SPLs) of the voice signal at a fixed distance from the speakers’ mouth after thecalibration procedure with a reference microphone [12].

Figure 1 - An example of long-term voice monitoring in a classroom during P2 with the lighting of SEM
device switched on. The contact microphone connected to the smartphone are marked white dots shape.
The voice monitoring procedure was performed according to past works in which the teachers’
voice use was performed during their activity for long-terms, i.e. either one week [13] or one year [14]monitoring. In short, the voice monitoring consisted in two steps, that is, in a calibration phase4098

performed in a quiet room of the school and in an activity monitoring performed in the teachingclassroom. The calibration procedure was performed before each long-term voice monitoring using aclass 1 sound level meter with omnidirectional microphone in air (SLM, XL2, NTi Audio) in theschool library, where the A-weighted equivalent background noise level was measured in threedifferent days. The average LA eq value of 37.6 dB (SD = 2.6 dB) was obtained. Each teacher was
equipped with the contact-microphone connected to the smartphone and then they were asked toperform the following steps: a) vocalizing for 3 to 5 short times the vowel /a/ with increasing intensity
and to repeat this task other two times, alternating few seconds of silence between the repetitions, b)
vocalizing a sustained vowel /a/ maintained from 5 to 10 sec and c) performing a free conversation of
about 1 min using a comfortable pitch of voice in any position of the room. The calibration steps a) andb) were carried out by each teacher standing in front of the omni-directional microphone in air at the
distance of 17cm on axis. Step c), instead, was performed with the teacher standing randomly in theroom at one meter from the researcher who supervised the monitoring.
2.3.3 Data processing of voice monitorings
Data were transferred from the sound level meter and smartphone to a personal computer forpost-processing. Only the part a) of vowel /a/ scales of the calibration procedure and the time-slot
regarding to plenary lesson have been selected at the aim of understanding the teachers’ voice use inrelation with the noise monitoring with SEM in P1 and P2. For the lessons’ monitoring, three different
time bands of the day were chosen according to the morning and lunch break, which discriminate
different loads for the teacher, that are: morning in the time ranges 8.30-10.30 (M1) and 10.45-12.30(M2), and afternoon in the time range 14.30-16.15 (A3). Only plenary lesson slots were selected
within the entire voice monitorings as this type of vocal activity is the most demanding for teachers,
due to a typical phonation time percentage of about 30% according to literature [13, 15]. Time-slots ofthe plenary lessons have been detected using Audacity software based on the information reported byteachers in the timesheets (described in Section 2.4). According to this procedure, more than one
signal has been defined for some long-term voice monitoring, thus a total of 32 signals have been
analyzed. The duration of the time-slot varied between 25 min to 120 min each.
Ad hoc MATLAB 2017A scripts were used to estimate the calibration curves of the vowel scale
signals recorded by the contact microphone and the SLM for each teacher in each voice monitoring.
Then, atemporal alignment between the two signals was performed in order to select the same time
segments, containing one or more vowel /a/ scales. With another proper MATLAB script, the voiced
and unvoiced frame detection through a suitable RMS voltage threshold was implemented. The RMS
values of each evaluated frame was compared to a threshold value defined during the calibration phase,and only the over-threshold frames, 0 and SPLs were calculated; conversely, zero values wereassigned to the under-threshold frames. As a main output, SPLs occurrence histograms from voiced
frames with a bin resolution of 1dB were obtained. Therefore, mean, median, mode and standard
deviation values could be calculated from such histograms. The results related to SPLmean,1m arereported in this study.
2.4 Subjective assessments
Two questionnaires were administrated to teachers. During each long-term monitoring, the changein activity (e.g. plenary lesson, shared lesson, group activity) was noted by teachers in a timesheet,
where they also indicated 1) their perceived vocal status at the end of the daily monitoring compared tothe beginning of the day, 2) the perceived degree of noise level in the classroom with respect to thesituation of empty room and school at the beginning of the day, 3) the change in voice intensity
compared to an ideal condition without background noise and reverberation [8]. The second
questionnaire presented in Astolfi et al. [3] was administrated at the end of the noise monitoring
campaign to investigate teachers’ perception on acoustic quality in relations to several variables. Somequestions about the SEM device were added to evaluate how teachers assessed its use as an educational
tool. In particular, three questions were related to the purpose of this study since they explored 1) theperceived vocal effort during lessons, and 2) the perceived reduction of vocal effort and 3) theperceived decrease of background noise levels generated by the presence of the lighting feedback of
the SEM device.

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3. RESULTS
3.1 Statistical analysis
The statistical analysis of the data was performed using SPSS software (v. 20 for Window; IBM Cor,
Armonk, NY). The normal distribution of parameters related to background noise levels and teachers’
voice was investigated through Shapiro–Wilk test considering a confidence interval of 95%(significance level α = 0.05), and an outlier analysis was performed applying the Mahalanobis
distance.
The one-tailed Mann-Whitney U Test (MWU) [16] was assessed to investigate statisticallysignificant differences between background noise levels (LA90 ) measured in two days related to P1 andP2 at the same time of the teachers’ voice monitoring. To reduce the number of variables, theoccurrence distributions of LA90 values related to both phases were selected maintaining some of themas fixed, e.g. teacher, plenary lesson, class and time band. Figure 2 shows an example of improvementin the monitored behavior of children related to their noise production, thus corresponds to a couple ofsamples where LA90,mean significantly decreased in P2 compared to P1.

Figure 2 – An example of occurrences distributions of background noise levels measured in class II C withteacher e), and the results of descriptive statistics and the MWU Test subdivided according to P1 and P2.
3.2 Relationship between background noise levels and voice parameter
Figure 3 shows the relationship between background noise levels (LA90,mean) and voice parameter(SPLmean,1m) through the best-fit regression lines of the data sample subdivided in Group 1 (n = 17) andGroup 2 (n = 15) according to the two phases, respectively. No data was excluded for this analysis.
Looking at the background noise levels, the smaller range of values was obtained for Group 2, (47dB to 59 dB) compared to Group 1 (51 dB to 62 dB). A slight difference of about 2 dB emerged fromthe comparison between the range of voice levels of both groups, with the lower range of values foundfor Group 2. The extreme point of Group 2 was excluded since it largely differed from all other values.
A tendency towards Lombard effect occurred when the lighting feedback of the SEM device wasswitched off (P1), i.e. an increase in the speech level with background noise level at a rate of about 0.4
dB/dB (Group 1), thus it is within the range indicated by Lazarus [17]. Conversely, a lower tendency toraise voice levels was obtained when a decrease of background noise levels was emerged according to
the presence of lighting feedback. Considering the small sample size of this pilot study and theinter-subject differences, the R-squared values were very low for Group 1 (R 2 = 0.16) and 2 (R 2 = 0.05),
and all the obtained relationships can be considered as tendencies only.

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Figure 3 - Best-fit linear regressions between the voice parameter (SPLmean,1m) and the mean backgroundnoise levels (LA90,mean) subdivided in Group 1 and 2 according to P1 and P2.
3.3 The effects of SEM device on background noise levels
Table 2 shows the results related to the analysis on background noise levels performed according to
MWU Test. Some data were excluded according to the procedure described in Section 3.1, thus 13
coupled samples were analyzed on a total of 32 data samples. Looking at the comparison between themean values of background noise levels (LA90,mean) measured in P1 and P2 a statistically significant
improvement was obtained for 5 of 13 coupled samples (38% in total). The average decrease of
LA90,mean values between these coupled samples was of 2.9 dB and a lower range of background noise
values was obtained in P2 (47.8 to 56.5 dB) compared to P1 (50.9 to 62.0 dB).
Table 2 – Comparison between LA90,mean measured in P1 and P2 for each coupled sample, and the differenceof LA90,mean values between P2 and P1. Significant improvements (p-value < 0.05 according to the MWU
Test) are reported in bold. The time band related to the working day are indicated with M1 (8.30-10.30) orM2 (10.45-12.30). The chronological number of the long-term voice monitoring are also specified.
Teacher

Time

ID

ID

band

P1

P2

P1

P2

[dB]

IL

a

M1

1

2

54.9

55.6

0.7

M2

1

2

62.0

56.5

- 5.5

M1

1

3

50.9

51.2

0.2

M2

1

3

54.2

55.0

0.7

M1

1

4

50.9

53.9

3.0

M2

1

4

54.2

55.3

1.1

M1

1

2

50.9

47.8

- 3.1

M2

1

2

53.9

50.4

- 3.5

M1

1

2

54.1

54.2

0.0

M1

1

3

54.1

53.2

- 0.9

M2

1

3

54.4

63.3

8.9

M1

1

2

53.9

54.2

0.2

M2

1

2

53.9

50.4

-1.6

II M

II N

IV O

d

e

f

g

Voice monitoring

LA90,mean [dB]

∆LA90,meanGrade class4101

3.4 Subjective assessments
Teachers self-estimated that the degree of noise level at the end of the day with respect to thebeginning of the day and the empty room were slightly better in presence of SEM device compared to
the working day without it, as well as their vocal status. At the end of noise monitoring campaign, 57%
of teachers declared that lessons generated a higher vocal effort compared to a normal conversation inabsence of noise and reverberation. However, SEM device was considered useful for reduction of
vocal effort and improvement of speech, and for decrease of noise levels generated by pupils during
plenary lessons for 86% and 72% of teachers, respectively.

4. DISCUSSION AND CONCLUSIONSThe presented work investigated in-field with a structured monitored campaign the reduction of
background noise levels generated by the changing of pupils’ behavior and its relationship with theteachers’ vocal effort, which approach and outcomes miss in the available literature so far. In theframework of this pilot, a statistically significant decrease in background noise levels was measured
during plenary lessons suggesting a possible change in pupils’ behavior according to the presence ofthe lighting feedback of the noise monitoring system. Moreover, the usefulness of the SEM device asan educational tool able to improve classroom environment was perceived by most of the teachers
involved in the study. These objective and subjective findings are in line with two previous studies [57], where the use of visual noise feedback devices was suggested as a possible low-cost solution to
reduce noise and improve teaching experience in classroom.
Concerning the relationship between background noise levels and vocal effort the results suggested
a tendency of teachers monitored over the weeks to raise their speech level less when the lighting
feedback of SEM device was switched in classrooms. Since the statistical power of these results is low,
particularly due to the small sample size, further investigations especially with larger numbers of voice
monitoring are needed to evaluate the changing in teachers’ vocal behavior generated from moreawareness on noise conditions and from the reduction of background noise based on the presence ofthe noise monitoring system with lighting feedback.

ACKNOWLEDGEMENTS
The authors are thankful to the teachers and pupils of the primary school “Roberto d’Azeglio” inTurin for their availability and patience, and also to Alessia Griginis of “Onleco S.r.l.” and Stefano
Cerruti of “Bottega Studio Architetti” since they collaborated in the development of SEM devices.

REFERENCES
1. Puglisi GE, Prato A, Sacco T, Astolfi A. Influence of classroom acoustics on the reading speed: A casestudy on Italian second-graders. J. Acoust. Soc. Am. 144; EL144 (2018). EL144-EL149.
2. Astolfi A, Bottalico P, Accornero A, Garzaro M, Nadalin J, Giordano C. Relationship between vocal
doses and voice disorders on primary school teachers. Proc 9th Conference on Noise Control Euronoise 2012; 10–13 June 2012; Prague, Czech Republic 2012.
3. Astolfi A, Pellerey F. Subjective and objective assessment of acoustical and overall environmental
quality in secondary school classroom. J. Acoust. Soc. Am. 2008; 123(1):163-7173.
4. Sato H, Bradley JS. Evaluation of acoustical conditions for speech communication in working
elementary school classrooms. J. Acoust. Soc. Am. 2008; 123(4):2064-77.
5. Prakash S, Rangasayee R, Jeethendra P. Low cost assistive noise level indicator for facilitating thelearning environment of school going learners with hearing disability in inclusive educational setup.
Indian Journal of Science and Technology 2011; 4(11):1495-150.
6. SoundEar. Available online: https://soundear.com/ (accessed on 22 May 2019)
7. Tonder JV, Woite N, Strydom S, Mahomed F, Swanepoel DW. Effect of visual feedback on classroom
noise levels. South African Journal of Childhood Education 2015, 5 (3)8. Astolfi A, Puglisi GE, Pavese L, Carullo A. Long-term vocal parameters of primary school teachers andclassroom acoustics with and without an acoustical treatment. Proc of Forum Acusticum 2014, 7-12
September 2014; Krakow 2014.
9. Di Blasio S, Vannelli G, Shtrepi L, Puglisi GE, Calosso G, Minelli G, Murgia S, Astolfi A. Long-termmonitoring campaigns in primary school: the effects of noise monitoring system with lighting feedback4102

on noise levels generated by pupils in classrooms. Proc of Inter.noise 2019, 16-19 June 2019; Madrid,
Spain 2019.
10. Carullo A, Vallan A, Astolfi A. Design issue for a portable vocal analyzer. IEEE Trans. Inst. Meas. 2013,
62(5): 1084–1093.
11. Carullo A, Penna A, Vallan A, Astolfi A, Pavese L, Puglisi GE. Traceability and uncertainty of vocal
parameters estimated through a contact microphone. Proc of IEEE International Symposium on Medical
Measurements and Applications 2014, 11–12 June 2014; Lisbon, Portugal 2014.
12. Carullo A, Vallan A, Astolfi A, Pavese L, Puglisi GE. Validation of calibration procedures anduncertainty estimation of contact-microphone based vocal analyzers. Measurement 2015; 74:130–142.
13. Puglisi GE, Cantor Cutiva LC, Astolfi A, Carullo A. Four-day-follow-up study on the voice monitoringof primary school teachers: Relationships with conversational task and classroom acoustics. J. Acoust.
Soc. Am. 2017; 141(1):441-452.
14. Calosso G, Puglisi GE, Astolfi A, Castellana A, Carullo A, Pellerey F. A one-school year longitudinal
study of secondary school teachers' voice parameters and the influence of classroom acoustics. J. Acoust.
Soc. Am. 2017; 142(2):1055-1066.
15. Hunter EJ, Titze IR. Variations in intensity, fundamental frequency, and voicing for teachers in
occupational versus nonoccupational settings. J. Speech Lang. Hear. Res. 2010; 53:862–875.
16. Sigel S, Castellan NJ. Non Parametric Statistics for the Behavioral Sciences, 2nd ed. McGraw-Hill:
New York, USA; 1988.
17.Lazarus H. Prediction of Verbal Communication is Noise – A review: Part 1. Applied
Acoustics. 1986;19(6):439–464.

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