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

Acoustic and subjective evaluation of Brazilian Portuguese speechrecordings made in critical listening environments
Ticiana Matar DE LELLO1; Stephan PAUL2; Luiz Wagner Pereira BISCAINHO3
1
2

PEE/COPPE - Federal University of Rio de Janeiro, Brazil

Mechanical Engineering Department - Federal University of Santa Catarina, Brazil
3

PEE/COPPE & DEL/POLI - Federal University of Rio de Janeiro, Brazil

ABSTRACTThe present work aims to characterize Brazilian Portuguese speech in realistic environments by means of
acoustical parameters and subjective perception, hoping to provide data for research on auditory prostheses.
Speech signals were recorded in several non-controlled environments such as restaurants, classrooms andstores. Sound pressure level, loudness and spectral content were extracted for speech and background noise
separately by using recording segmentation and spectral subtraction. Subjective perception was analyzed byapplying listening tests with samples from the recordings. Data analysis showed an increase of 0.6 dB in the
speech’s SPL per each dB increase in the background noise’s SPL and a great difference in overall loudness
between the quietest and the noisiest environments. SPLs were also mostly coherent to those previously
reported in the literature for the same type of environment. The listening tests’ results, while also greatly
influenced by personal taste, showed that the annoyance caused by background noise, impairment in speech
intelligibility, and the effort to understand speech were correlated to the background noise level and the SNR.
In general, the environments perceived as the most critical for spoken communication were those in whichbackground noise presents the highest energy concentration in the spectral region of speech.
Keywords: Speech recordings, Acoustic parameters, Subjective tests

INTRODUCTION
Auditory prostheses, such as hearing aids (HA), middle ear implants and cochlear implants (CI)are essential for hearing impaired individuals worldwide. They offer a better hearing experience inthe user's everyday life and can provide an increase in life quality. However, even if auditory
prostheses possibly help in increasing audibility and speech intelligibility in quiet environments, it
can be seen that users usually still experience problems in environments with significant backgroundnoise and reverberation (1) or in situations with several speakers talking simultaneously.
As these characteristics are very frequent in daily life, it is necessary to study the acousticalcharacteristics of speech and background noise in everyday situations and its influence on subjective
perception, in order to help developing prostheses which are more adapted to realistic acousticalscenarios. This is even more urgent for Brazilian prostheses' users, due to the lack of research on this
topic considering Brazilian Portuguese speakers, Brazilian environments and the peculiarities of theircultural and socio-acoustical contexts.
In this work, recordings of speech signals were obtained in multiple non-controlled environments
common to everyday life, such as classrooms, restaurants and commercial establishments. Speech andbackground noise levels were extracted from the recordings, analyzed and compared with previously
obtained data (2, 3, 4). Loudness levels and spectral content for speech and background noise werealso computed. Lastly, listening tests were performed using sound samples from the recordings toverify the association between subjective perception and acoustic parameters.

1

ticiana@poli.ufrj.br
stephan.paul@ufsc.br3
wagner@smt.ufrj.br
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1. METHODS
1.1 Extraction of acoustic parameters
First, calibrated binaural speech signal recordings were obtained in seven restaurants (R1–R7),
five classrooms (C1–C5), four commercial establishments inside a shopping mall (M1–M4), a livingroom in a domestic environment (LR1, LR2), an airport's boarding area (A1) and several environments
within a university campus (O1–O3). The recordings were then divided into small segments where themain speaker's speech is present (regarded as "speech segments") and segments where it is absent andthere is only background noise, composed by all the other sound sources in the acoustic scene,including other people's voices (regarded as "non-speech segments"). This allowed the extraction of
acoustic parameters of speech and background noise separately. The segments were cut alternately(non-speech, speech, non-speech,...) and have durations which span from tens of milliseconds up to
one minute. This was done manually with the HEAD Acoustics' Artemis SUITE® software.
Sound pressure level estimates were then obtained for speech and background noise for eachrecording, or part of recording. The calculations were done equally for A and C frequency weightings(5). An equivalent background noise level 𝐿 ,was obtained for the set of non-speech,segments (NS), and an equivalent overall level 𝐿 ,, , which corresponds to the main
speaker's speech and background noise combined, was obtained for the set of speech segments (S) in
the recording. Letter X here is a placeholder for A- or C-weighting generically. Therefore, abackground noise SPL estimate 𝐿 ,, is calculated for a speech segment 𝑖 based on the previous
and subsequent non-speech segments, respectively, as follows:
.
,
, ,
,
, ,
𝐿 ,
+ 10 .
)](1), , = 10log[1/2(10A SPL can now be estimated for the sole speech contribution in this segment:.
,, , − 10 .
,, , )](2)𝐿 ,, , = 10log[1/2(10This procedure was done for each group of three segments in the sequence NS−S−NS (non-speech,
speech and non-speech segment, respectively), regarded as triples, inside each recording, and for A
and C weightings. In some cases, the energetic mean that corresponds to the background noise level
estimate 𝐿 ,, in the speech segment was found to be greater than the segment's total level𝐿 ,, , when the latter is smaller than the total levels of the adjacent non-speech segments.
These cases, which are less than 5% of all the segments, were considered invalid and removed fromthe sound pressure level analyses.
Overall Zwicker loudness over time for each recording was also calculated, which allowed toobtain an equivalent loudness for background noise for non-speech and speech segments. This wasdone with a similar method to that described in Equations 1 and 2, but using the arithmetic instead ofthe energetic means. The impossibility to isolate the loudness contribution of speech alone due to thecomplexity of the loudness model allowed only a comparison between the background noise loudness𝑁, and the overall loudness 𝑁, of the speech segment, composed by backgroundnoise and the main speaker's speech.
Lastly, estimates of the speech and background noise spectra were obtained for each recording,with unweighted SPLs (5). An equivalent background noise spectrum for each recording was obtained
as an energetic mean of the spectra of the recording's non-speech segments (which carried informationof background noise only). The same procedure was repeated for the speech segments, generating anequivalent spectrum for the speech and background noise combination. This allowed an estimation
for the recording's equivalent speech spectrum by using the spectral subtraction procedure describedin Equation 3, where 𝑖 is a spectrum bin, 𝐿
is the bin's total level, 𝐿 is the bin's
background noise SPL and 𝐿 is the computed speech SPL. A factor 𝛽 = 0.01 was chosen empiricallyto correct occasional results below 0 dB. The resulting spectrum is then trimmed to the fundamental
frequency region (approximately 100 Hz for men and 200 Hz for women), removing frequency content
below the fundamental frequency.
𝐿, =10 log(|𝛽 ∙ 10 .
10 log(10 .

,if 10 .
, − 10 .
, ),

|) ,

− 10 . , < 0;otherwise.
,

(3)

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1.2 Listening tests
In order to analyze the relationship between acoustic parameters and subjective perception, alistening test was implemented with 23 audio samples from some of the previously obtained
recordings. The listening test was completed by 22 students of the Federal University of Rio deJaneiro−eight women and fourteen men−aged between 22 and 29, who reported no hearing loss. The
tests were conducted in an acoustically treated room. Each subject should listen to the audio sample,
always played back over the same circum-aural headphones, with calibrated volume and preservationof the two original audio channels. Answers were collected by a computer interface.
Before starting the tests, all subjects read a manual containing all the procedure's instructions. The
manual made explicit that there was always a main speaker in each sample, whose voice was in theforeground and who spoke most of the time, even if there were occasionally other speakers in the
background. Each sample had at least one and no more than two main speakers. Subjects were
therefore instructed to identify the main speaker in the sample and answer the items considering themain speaker's speech as reference. Voices from other speakers eventually present in the sample
should be then considered background noise, along with the other sounds in the sample.
The test's interface allowed a sample to be played as many times as desired and displayed three
items (questions):
1. What is the degree of annoyance of the ambient noise, disregarding the main speaker's speech?
2. To which degree is the intelligibility of the main speaker's speech hindered by the ambient
noise?
3. How much effort is required to understand the content of the main speaker's speech?
Each item was associated to a line scale whose ends were labeled NONE/NOTHING and
EXTREME/EXTREMELY. The subject was instructed to place the cursor at the position in the scale
which best represented his/her perception. The interface also displayed an "Observations" field, which
could be optionally completed in case the subject desired to make comments or share any impressions
regarding the sample.
The complete test featured twenty-three runs, each run with one audio sample of about 30 seconds.
The first three runs were used for training and therefore removed from the results. The next twenty
samples were then played in random order.

2. RESULTS AND DISCUSSION
2.1 Analysis of acoustic parameters
The A- and C-weighted speech SPL 𝐿 ,, and noise SPL 𝐿,, obtained by Equations
1 and 2 for each speech segment are plotted in Figure 1. An approximately linear relationship between
the two variables can be seen for both weightings. Linear regressions were performed with the leastsquares method, in which 𝑅 is the squared Pearson's linear correlation coefficient (6).

(a) A-weighting(b) C-weighting

Figure 1 – 𝐿 versus 𝐿 . Each environment type is represented by a particular color. Speech SPL as afunction of noise SPL shows a growth rate of 0.6 dB/dB. 𝑅 > 0.7 for both weightings.

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Both graphs show a 0.6 dB increase in speech level for each dB increase in noise level. This rate
corresponds to that found in Pearsons et. al.’s study (2), in which A-weighted speech and noise SPLs
were measured in many different environments. For background noise SPLs above 48 dB(A)−whichis the case for all environments studied here−the authors observed this same rate of 0.6 dB/dB for all
face-to-face communication situations in all environments with the exception of classrooms. This rate
does not apply to each environment type in this work separately, but to the entire set of points that
represent the noise and speech levels in all environments. Due to the similar relations found for bothA- and C-weightings and to the comparison with other studies which use A-weighting only, it was
decided to present only the A-weighted sound pressure levels for the subsequent sound pressure level
analyses shown in this article.
Table 1 shows the arithmetic means 𝐿 ,and 𝐿 ,
in some of the environments. The
table also shows some speech and noise SPLs found in other studies for comparison: “Living room
(casual talk)” and “Shopping mall (stores)” are compared to the levels obtained by Pearsons et. al. (2)for domestic environment and department stores, respectively; “Restaurants”, to those of To andChung (3); “Shopping mall (snack bar)”, to an average of the noise levels measured in the food courtsof four shopping malls by Carvalho and Pereira (4); and “Classrooms”, to the averages of speech andnoise levels found for two classrooms in Pearsons et. al. (2) for a speaker-listener distance of 7 meters.
Restaurants are classified by occupational density, considered, according to To and Chung's criteria(3), as high (80-100% occupation), medium (60-79%) or low (40-59%). Speakers and listeners are atface-to-face communication distance in most situations (0.3−1 m), with the exception of the classroom
scenario (5−9 m) and the living room scenario, in which the distance is 1 meter for the measurements
in Pearsons et. al. (2), and within the range of 2 to 5 meters in the recordings performed in this study.
Table 1 – Average speech and background noise levels in different environments
Environment

𝑳𝑨𝐞𝐪,𝐧𝐨𝐢𝐬𝐞 /Other studies (dB(A))

𝑳𝑨𝐞𝐪,𝐬𝐩𝐞𝐞𝐜𝐡 /Other studies (dB(A))

Restaurants (high)

79/77 (3)78/78.58 (3)Restaurants (medium)76/73 (3)77/75.53 (3)Restaurants (low)68/69 (3)74/72.83 (3)Living room (casual talk)

46/48 (2)64 (2-5 m)/57 (1 m) (2)Shopping mall (stores)

70/54 (2)74/61 (2)Shopping mall (snack bar)

83/69 (4)82Classrooms

56.2/51 (2)63.1/59.5 (2)Substantial differences between levels measured in this and other studies can be seen for the
shopping mall environments (stores and snack bar). For stores, in particular, a higher concentrationof people and loud music in the background could be possible causes for the higher speech and noise
SPLs in this work, in comparison to those present in that type of environment by the time of Pearsons
et. al.'s work (2), in 1977. The noise SPL found in this study for the snack bar, on the other hand, ismore similar to that found in highly occupied restaurants in To and Chung (3) and to that measured infood courts by Carvalho and Pereira (4). A possible cause for the higher speech SPL in the living roomin comparison to that of Pearsons et. al. (2) was the raised vocal effort used by the speaker in thisstudy, who was also further away from the listener.
Speech and noise SPLs in classrooms also have slightly greater values in comparison to those ofPearsons et. al. (2). The acoustical conditions of the classrooms in that study is unknown, but climate
differences between United States and Brazil suggest that the classrooms used by Pearsons et. al. formeasurements had no influence of air conditioner or fan noise, and probably feature better soundabsorption and sound insulation, which also reduce the influence of external noise.
The relation between the background noise loudness and the total loudness is also similar to thatfound for speech and background noise SPLs. There is a growth rate of 1.1 sone/sone for the total
loudness as a function of the background noise loudness. For the quietest places, such as classrooms,
there is an average total loudness of 16 sone, against 9 sone from background noise only. In thenoisiest restaurants, on the other hand, the total loudness can exceed 60 sone, while background noise
loudness can be of 50 sone or even higher. Therefore, background noise in the noisiest environmentsof this study can be perceived as up to six times louder than that found in the quietest ones.

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The speaker’s contribution to the total loudness, however, decreases as the background noise
loudness increases. One could understand that, in an already very noisy place, a small increase in the
total sound energy produced by the speaker’s voice would not make as much of a difference for thelistener’s sound volume sensation as it would in a very quiet place. For the quietest environments
studied here, background noise loudness corresponds to 50% of the total loudness, while it is only
17% smaller than the total loudness in the noisiest environments.
The 1/3-octave spectra also illustrate the acoustical differences within the environments andsituations, as can be seen in Figure 2, for six different situations: living room in a casual talk situation(LR1), living room in a party conversation situation (LR2), a teacher giving a lecture in classroom
C1, a speaker talking to his listener across the table in restaurant R4, a face-to-face communication
inside a shopping mall boutique (M1), and the voice transmitted through loudspeakers announcing
flights inside a boarding area of an airport (A1). Quiet environments, such as classrooms and theliving room in the casual talk situation, have in common a “flatter” background noise spectral shape,
clearly distinguished from the speech’s spectral shape; speech SPLs rise clearly above backgroundnoise SPLs in most, if not nearly all, 1/3-octave bands. Even so, a great individual difference between
speakers could be seen for some classrooms, such as classroom C5, which had a teacher and a student
making a presentation speaking at the same position in front of the class in different moments of thesame lecture. As seen in this example, different talkers in the same classroom produced different
shaped speech spectra and each had a different average speech level.
Background noise in the noisier environments, such as restaurants, shopping mall establishments
and the boarding area of an airport, on the other hand, had a more complex spectral shape. It can beseen that background noise in those places exhibits a high energy concentration in the same spectral
band as speech, between 100 and 2000 Hz, approximately. Speech is then largely masked bybackground noise, which explains the speaker’s need to often raise his voice significantly in an
attempt to be heard and understood (Lombard effect). Sound sources in those noisy environments
which contribute to the increase in background noise are often music (in case of stores, restaurants
and the home party), typical noises associated to that environment (e.g. the sound of dishes and cutlery
in restaurants), and lastly, and mainly, other people’s voices in the background. The simultaneous
speech of multiple background talkers is the main factor for the energy accumulation of backgroundnoise in the spectral region of the main speaker’s speech and for approximating the spectral shape ofbackground noise to the spectral shape of speech itself. This can potentially cause confusion in thelistener, worsen the communication conditions and speech intelligibility.

Figure 2 – Spectra of background noise (dotted line, black) and speech (solid line, blue or red) for someenvironments.

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2.2 Subjective perception
The cursor positions chosen by the test subjects as answers for the three items in each test run wereconverted to integers between 0 (corresponding to the NOTHING end) and 100 (corresponding to the
EXTREME/EXTREMELY end). The answers of each subject were analyzed. One of the criteria to
consider a subject’s response as an outlier was the presence of something that evidenced a lack ofcorrect understanding of the test instructions. According to this, responses of two subjects wereconsidered as outliers and removed from the data set, given that the incoherence in their answers
seemed to show and their noticeable discrepancy with the other subjects’ answers evidenced adifficulty in understanding the items’ meanings and the line scales.
Problems with the identification of the main speaker occurred only for the sample A1 (airport), in
which the main speaker actually corresponded to the voice announcing the flights through theloudspeakers, instead of any other speaker in the background, which confused some of the subjects.
Due to this, only the answers of nine participants that had been explicitly warned about this detail
before the test run were considered for this sample.
Figure 3 shows the average values and the standard deviation for the answers to each item.
Classrooms C1, C2, C4 and C5 have two samples each. When there is the same main speaker in both
samples, samples are distinguished by letters “a” and “b”. When each sample has a different main
speaker, a number is used (e.g. C1.1 and C1.2 refer to Speaker 1 and 2 in the same classroom at
different moments). There is a large standard deviation for most items, showing the differences inindividual perception. Even so, the average values behaved similarly to what was expected, with themost critical scenarios such as those in restaurants and at the snack bar inside the shopping mall (M4)receiving the worst (highest) grades in all items.

Figure 3 – Average punctuation per sample, for all three items.
There is strong correlation between the average grades of Items 2 and 3 (𝑟 = 0,97, 𝑝 < 0,05), Items
1 and 2 (𝑟 = 0,84, 𝑝 < 0,05), and Items 1 and 3 (𝑟 = 0,72, 𝑝 < 0,05). As expected, a high degree ofannoyance by the background noise is associated to greater impairment in speech intelligibility andgreater effort to understand speech. On the other hand, a great amount of effort to understand speechis usually, but not always, associated to annoying background noise.
A demonstration that a great amount of effort to understand speech is not always related tobackground noise itself are the grades given to samples C5.1 and C5.2. Grades given to sample C5.2
are noticeably better (lower) due to the higher speech SPLs and better utterance of Speaker 2 (student),
in comparison to Speaker 1 (teacher), even though background noise is low, not annoying and almost
identical in both samples. Other cases which received moderately high grades for Item 3 despite
receiving low grades for Item 1 have these grades possibly related to the speaker’s utterance, speech
speed, speech style and, in some cases, accent. For classrooms, a possible influence of reverberation
time in speech intelligibility was also presumed.

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On the other way around, grades given for Item 1 were higher than expected for cases which
received moderately high grades for Items 2 and 3 – mainly, when the speech level is pretty low. This
could mean that background noise may become more annoying simply by the fact that the listenercannot understand the speaker well enough.
Also very important was the influence of personal taste. Party noise in sample LR2 and thebackground music inside the boutique in sample M1, for example, were extremely annoying for somesubjects, while others considered them actually not annoying at all. Some subjects reported
impressions in the “Observations” field that also showed interesting aspects of subjective perception,such as annoyance caused by the cutlery noise in restaurants and by air conditioner noise in classrooms,
sleepiness caused by softly-speaking teachers in classrooms and even having a hard time notconcentrating on the music in the background in some samples.
Lastly, the grades given to each sample were compared to the previously obtained acousticalparameters and spectra of the corresponding recordings. Since the samples were chosen and edited inlength for the listening tests after the acoustical parameter extraction from the recordings had alreadybeen made, and since a repetition of this procedure for the samples themselves could not be done forlogistical reasons, the comparison was made with previously measured parts of the recordings which
had the largest possible overlap with the samples created for the listening tests. Table 2 shows thecorrelation of the average grades received by the samples to the average speech and background noise
SPLs, to the signal-to-noise ratio, to the average total loudness 𝑵𝐞𝐪,𝐬𝐩𝐞𝐞𝐜𝐡 𝐧𝐨𝐢𝐬𝐞 and to the backgroundnoise loudness 𝑵𝐞𝐪,𝐧𝐨𝐢𝐬𝐞 , found for the parts of the recordings representing the samples.
Table 2 – Correlation of average grades given to Items 1, 2 and 3 and acoustical parameters
SNR

𝑳𝑨,𝐞𝐪,𝐬𝐩𝐞𝐞𝐜𝐡

𝑳𝑨,𝐞𝐪,𝐧𝐨𝐢𝐬𝐞

𝑵𝐞𝐪,𝐬𝐩𝐞𝐞𝐜𝐡 𝐧𝐨𝐢𝐬𝐞

𝑵𝐞𝐪,𝐧𝐨𝐢𝐬𝐞

𝑟

𝑝

𝑟

𝑝

𝑟

𝑝

𝑟

𝑝

𝑟

𝑝

Item 1

0.82

<0.05

0.95

<0.05

-0.71

<0.05

0.95

<0.05

0.96

<0.05

Item 2

0.49

<0.05

0.74

<0.05

-0.77

<0.05

0.66

<0.05

0.75

<0.05

Item 3

0.3

0.2

0.59

<0.05

-0.74

<0.05

0.56

<0.05

0.61

<0.05

Item 1 is strongly correlated to all parameters, and mostly to those directly related to backgroundnoise itself. This shows that, indeed, the degree of annoyance caused by background noise is mostly
proportional to the A-weighted background noise SPL. Nevertheless, it is important to note that the
average sample duration of 30 seconds gives a very short time for the listener to accommodate himself
to the ambient noise. With a longer accommodation time, grades given to the degree of annoyancecaused by the background would be possibly lower. The listening test situation is also an artificial
scenario, in which the subject focuses his attention to elements to which he otherwise would not inreal situations. It must be considered once again that factors other than SPL and loudness can be
related to annoyance, such as the timbre, the nature of the noise, and personal taste of the listener.
Item 2, while also strongly correlated to background noise SPL and loudness, is most strongly
correlated to the SNR, and Item 3 is only strongly correlated to the SNR. These items, therefore, do
not particularly relate to speech or background noise alone, but mainly to the way speech andbackground noise interact with each other–the more masked speech is by background noise, the moredamaging background noise is to intelligibility, and the more effort is required by the listener tounderstand speech, even if background noise itself is not that loud after all.
A final comparison can be made between the grades and the average spectra of speech andbackground noise of the corresponding recordings, or parts of recordings representing the samples.
The listening test grades tended to be always better (lower), especially for Items 2 and 3, for situations
in which the background noise spectrum does not have as much energy concentration in the spectral
region of speech or as much of a similar shape to that of the speech spectrum, as can be seen in Figure
2 for the casual talk situation in the living room (LR1) and for the conversation inside the boutique(M1), respectively.
In the noisiest environments, such the restaurants, the snack bar inside the shopping mall and the
airport boarding area, the main factor for the bad (high) grades in all three items is the fact thatbackground noise in those recordings is mainly composed by other people’s speech. The great amountof information sharing a similar spectral content to that of the main speaker’s speech masks the main
speech considerably. As can be seen in Figure 2 for restaurant R4 and the airport boarding area (A1),6866the background noise spectrum in those cases is similar both in the 1/3-octave band levels and in
shape to the main speech spectrum itself (higher levels between 100 and 2000 Hz, with a local
maximum between 500 and 1000 Hz). As expected, this effect in fact contributed to confusion anddiscomfort felt by the listeners, represented by the subjects.
All the spectra obtained from the recordings and used for comparison with the listening test results
can be found in De Lello (7), as well as the entire work description in more detail.

3. CONCLUSIONSThis paper aimed to characterize Brazilian Portuguese speech in real world environments byanalyzing both the acoustical parameters and subjective perception regarding speech and backgroundnoise of binaural recordings obtained in several such environments.
Results showed mostly similarities in speech and background noise SPLs with data obtained byother researchers and reported in the literature. SPLs were also coherent to the loudness values.
Listening test results evidenced a tight relationship between the annoyance caused by backgroundnoise and its sound pressure level, while reduction in speech intelligibility caused by backgroundnoise and the effort to understand speech had a strong correlation to the SNR. Situations in whichbackground noise has a high energy concentration in the spectral region of speech were perceived as
the most critical for spoken communication.
Future works on this subject must consider a more detailed analysis of the social, behavioral andarchitectural factors in these spoken communication contexts, as well as a greater diversity ofBrazilian Portuguese accents, given that this study was conducted in Rio de Janeiro and had mostly
representatives of the local accent. Some tasks to be accomplished are extracting more informationfrom the speech signal, relating it to vocal effort; identifying the most prominent frequency regionsof speech in each type of environments; verifying the influence of diction, speech speed and accentin speech comprehension; and considering the influence of reverberation time in speech intelligibility.
The knowledge obtained on this subject could then contribute to the development of more adapted
auditory prostheses for hearing impaired people in Brazil and abroad, capable of providing users amore comfortable and natural experience in everyday life communication situations.

ACKNOWLEDGEMENTS
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível
Superior - Brasil (CAPES) Finance Code 001. Thanks also to the National Council for Scientific andTechnological Development (CNPq), which gives support to the development of this work; to HEAD
Acoustics®, whose equipment SQuadriga II® and software Artemis SUITE® allowed the recordings
and the acoustical parameters’ visualization; and to GROM Acústica & Vibração, for lending theequipment and software, helping making this work possible.

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